https://de.wikipedia.org/wiki/Bezirk_Schwaz

http://www.skimap.info/europe/austria/tirol/

https://prime-skiing.de/wp-content/uploads/2021/09/tirol_snow_card_gebiete_uebersicht_saisonkarte_2020-2021.jpg

https://www.apoplous.blog/the-alps/

• Alpbachtal
• Axamer Lizum
• Elfer lifte
• Fendels
• Fieberbrunn
• Fügen-Spieljochbahn
• Hintertux
• Hochfügen-Hochzillertal
• Ischgl
• Kaunertaler Gletscher
• Kitzbühel – Kirchberg
• Kühtai
• Mayrhofen
• Nauders
• Obergurl Hochgurgl
• Oetz
• Pillersee, Sankt Jakob
• Pitztal – Hochzeiger
• Pitztaler Gletscher – Rifflsee
• Schlick 2000
• Serfaus – Fiss – Ladis
• Serles Bahnen
• Silvapark – Galtür
• Sölden
• Steinplatte – Waidring
• Stubaier Gletscherbahnen
• Venet – Landeck, Zams
• Wildschoenau – Niederau, Auffach
• Zillertal Arena
  • Gerlos
  • Zell am Ziller
  • Königsleiten

🏔️ 齐勒塔尔及周边 —— 滑雪场、停车信息与住宿

🎿 Fahrzeiten & Parkmöglichkeiten inklusive Kartenlinks

🎿 包含导航链接的预计车程与停车信息

🎿 Skigebiete im Zillertal

🎿 齐勒塔尔滑雪场概览

Die Region Zillertal in Tirol bietet eine Vielzahl an Skigebieten. Die Anfahrt erfolgt über gut ausgebaute Hauptstraßen ohne nennenswerte Steigungen, Parkmöglichkeiten sind meist ausreichend vorhanden (Parkhäuser, asphaltierte Plätze, vielfach kostenfrei).

奥地利蒂罗尔的齐勒塔尔地区拥有多个滑雪胜地。道路宽阔平坦，几乎没有明显坡度。大多数滑雪场都提供充足的停车位（停车楼、柏油地面，很多免费）。

🚗 Fahrzeiten & Parkmöglichkeiten

🚗 预计车程与停车信息

📌 Übersichtskarten & Pistenpläne / 滑雪地图与信息

• Schneehoehen – Skigebiete Österreich
• Bergfex Hochficht
• Bergfex Zell am Ziller – Panorama
• Snowplaza – Zillertal Pistenplan
• Skiresort – Parkmöglichkeiten im Zillertal

🏡 Chalet Rastenhof – Unterkunft in Gallzein

🏡 Rastenhof木屋 —— Gallzein住宿

• Adresse / 地址: Niederleiten 28, 6222 Gallzein
• Website / 官网: rastenhof.at
• Größe / 面积: 280 m², geeignet für bis zu 14 Personen / 可容纳14人

Ausstattung / 设施

• 5 Schlafzimmer, 3 Bäder (Duschen + Badewanne)
• Sauna, Terrasse, Balkon, Garten, Grillplatz
• Private Küche mit Spülmaschine, Backofen, Kaffeemaschine etc.
• Kinderfreundlich: Hochstühle, Spielplatz, Babybetten auf Anfrage
• Wohnzimmer mit Sofa, Flachbild-TV, Minibar

Bezahlung / 支付方式

• 10 % Anzahlung per Überweisung innerhalb von 7 Tagen
• Restzahlung per Überweisung 2 Wochen vor Anreise
• 预订时需支付房费的10%，余款需在到达前2周转账支付

🌄 Region Gallzein & Karwendel

🌄 Gallzein与Karwendel地区

• Gallzein ist ein kleines, ruhiges Dorf auf ca. 800 m Seehöhe mit ca. 700 Einwohnern.
• Lage im Silberregion Karwendel, nahe Inntal und Zillertal.
• Ideal für Natururlaub, Wandern und Erholung.

Gallzein是一个宁静的小村庄，海拔约800米，约有700名居民。位于“银矿区”（Silberregion Karwendel），邻近因河谷和齐勒塔尔，适合亲近自然、徒步和度假。

📌 Weitere Ausflugsziele / 更多景点:

• Sehenswürdigkeiten rund um den Rastenhof
• Mayrhofen – Stories
• Bochra-See

✅ Fazit / 总结
Das Zillertal bietet ideale Bedingungen für Wintersport mit modernen Skigebieten und guten Parkmöglichkeiten. Das Chalet Rastenhof in Gallzein ergänzt dies perfekt als großzügige und komfortable Unterkunft für Familien und Gruppen.

齐勒塔尔地区滑雪条件优越，配备现代化滑雪场和完善的停车设施。Rastenhof木屋环境安静、设施齐全，非常适合家庭和团体入住。

奥地利“Top 10”滑雪胜地

奥地利的“Top 10”滑雪胜地没有统一的定义，因为不同排行榜使用的标准各异，例如滑雪场规模、雪况稳定性、家庭友好度或海拔高度。一些最常被提及的滑雪胜地包括：

• Skicircus Saalbach Hinterglemm Leogang Fieberbrunn
• SkiWelt Wilder Kaiser-Brixental
• Serfaus-Fiss-Ladis
• Sölden

其他受欢迎的滑雪胜地还包括：Kitzbühel、Zillertal Arena、Ischgl 和 Mayrhofen。

Top 滑雪胜地概览（按不同标准）

最大滑雪场

• SkiWelt Wilder Kaiser-Brixental：奥地利最大的连续滑雪区，拥有 270 公里雪道。
• Skicircus Saalbach Hinterglemm Leogang Fieberbrunn：非常大且受欢迎，雪况稳定。
• Silvretta Arena Ischgl-Samnaun：以雪况稳定和丰富的 après-ski 活动闻名。

最适合家庭的滑雪场

• Serfaus-Fiss-Ladis：以家庭特别是儿童设施闻名。
• Zillertal Arena：同样是家庭友好型滑雪场，雪道丰富。

海拔高且雪况好的滑雪场

• Sölden：拥有两座冰川，海拔最高可达 3,340 米。
• Hintertuxer Gletscher：全年 365 天可滑雪。
• Mölltaler Gletscher & Kaunertaler Gletscher：奥地利最高的滑雪场之一。

知名及受欢迎的滑雪场

• Kitzbühel (KitzSki)：著名且受欢迎的滑雪目的地。
• Mayrhofen（Zillertal）：滑雪爱好者的天堂，以雪道丰富著称。
• Ski Arlberg：连接蒂罗尔和福拉尔贝格的多个滑雪胜地，广受欢迎。

滑雪场雪道长度一览表

1. install mambaforge https://conda-forge.org/miniforge/ (recommended)

   #download Mambaforge-24.9.2-0-Linux-x86_64.sh from website
   chmod +x Mambaforge-24.9.2-0-Linux-x86_64.sh
   ./Mambaforge-24.9.2-0-Linux-x86_64.sh
   
   To activate this environment, use:
       micromamba activate /home/jhuang/mambaforge
   Or to execute a single command in this environment, use:
       micromamba run -p /home/jhuang/mambaforge mycommand
   installation finished.
   
   Do you wish to update your shell profile to automatically initialize conda?
   This will activate conda on startup and change the command prompt when activated.
   If you'd prefer that conda's base environment not be activated on startup,
     run the following command when conda is activated:
   
   conda config --set auto_activate_base false
   
   You can undo this by running `conda init --reverse $SHELL`? [yes|no]
   [no] >>> yes
   no change     /home/jhuang/mambaforge/condabin/conda
   no change     /home/jhuang/mambaforge/bin/conda
   no change     /home/jhuang/mambaforge/bin/conda-env
   no change     /home/jhuang/mambaforge/bin/activate
   no change     /home/jhuang/mambaforge/bin/deactivate
   no change     /home/jhuang/mambaforge/etc/profile.d/conda.sh
   no change     /home/jhuang/mambaforge/etc/fish/conf.d/conda.fish
   no change     /home/jhuang/mambaforge/shell/condabin/Conda.psm1
   no change     /home/jhuang/mambaforge/shell/condabin/conda-hook.ps1
   no change     /home/jhuang/mambaforge/lib/python3.12/site-packages/xontrib/conda.xsh
   no change     /home/jhuang/mambaforge/etc/profile.d/conda.csh
   modified      /home/jhuang/.bashrc
   ==> For changes to take effect, close and re-open your current shell. <==
   no change     /home/jhuang/mambaforge/condabin/conda
   no change     /home/jhuang/mambaforge/bin/conda
   no change     /home/jhuang/mambaforge/bin/conda-env
   no change     /home/jhuang/mambaforge/bin/activate
   no change     /home/jhuang/mambaforge/bin/deactivate
   no change     /home/jhuang/mambaforge/etc/profile.d/conda.sh
   no change     /home/jhuang/mambaforge/etc/fish/conf.d/conda.fish
   no change     /home/jhuang/mambaforge/shell/condabin/Conda.psm1
   no change     /home/jhuang/mambaforge/shell/condabin/conda-hook.ps1
   no change     /home/jhuang/mambaforge/lib/python3.12/site-packages/xontrib/conda.xsh
   no change     /home/jhuang/mambaforge/etc/profile.d/conda.csh
   no change     /home/jhuang/.bashrc
   No action taken.
   WARNING conda.common.path.windows:_path_to(100): cygpath is not available, fallback to manual path conversion
   WARNING conda.common.path.windows:_path_to(100): cygpath is not available, fallback to manual path conversion
   Added mamba to /home/jhuang/.bashrc
   ==> For changes to take effect, close and re-open your current shell. <==
   Thank you for installing Mambaforge!
   
   Close your terminal window and open a new one, or run:
   #source ~/mambaforge/bin/activate
   conda --version
   mamba --version
   
   https://github.com/conda-forge/miniforge/releases
   Note
   
       * After installation, please make sure that you do not have the Anaconda default channels configured.
           conda config --show channels
           conda config --remove channels defaults
           conda config --add channels conda-forge
           conda config --show channels
           conda config --set channel_priority strict
           #conda clean --all
           conda config --remove channels biobakery
   
       * !!!!Do not install anything into the base environment as this might break your installation. See here for details.!!!!
   
   # --Deprecated method: mamba installing on conda--
   #conda install -n base --override-channels -c conda-forge mamba 'python_abi=*=*cp*'
   #    * Note that installing mamba into any other environment than base is not supported.
   #
   #conda activate base
   #conda install conda
   #conda uninstall mamba
   #conda install mamba

2: install required Tools on the mamba env

    * Sniffles2: Detect structural variants, including transposons, from long-read alignments.
    * RepeatModeler2: Identify and classify transposons de novo.
    * RepeatMasker: Annotate known transposable elements using transposon libraries.
    * SVIM: An alternative structural variant caller optimized for long-read sequencing, if needed.
    * SURVIVOR: Consolidate structural variants across samples for comparative analysis.

    mamba deactivate
    # Create a new conda environment
    mamba create -n transposon_long python=3.6 -y

    # Activate the environment
    mamba activate transposon_long

    mamba install -c bioconda sniffles
    mamba install -c bioconda repeatmodeler repeatmasker

    # configure repeatmasker database
    mamba info --envs
    cd /home/jhuang/mambaforge/envs/transposon_long/share/RepeatMasker

    #mamba install python=3.6
    mamba install -c bioconda svim
    mamba install -c bioconda survivor

4. Test the installed tools

   # Check versions
   sniffles --version
   RepeatModeler -h
   RepeatMasker -h
   svim --help
   SURVIVOR --help
   mamba install -c conda-forge perl r

5. Data Preparation

   Raw Signal Data: Nanopore devices generate electrical signal data as DNA passes through the nanopore.
   Basecalling: Tools like Guppy or Dorado are used to convert raw signals into nucleotide sequences (FASTQ files).

6. Preprocessing

   Quality Filtering: Remove low-quality reads using tools like Filtlong or NanoFilt.
   Adapter Trimming: Identify and remove sequencing adapters with tools like Porechop.

7. (Optional) Variant Calling for SNP and Indel Detection:

   Tools like Medaka, Longshot, or Nanopolish analyze the aligned reads to identify SNPs and small indels.

8. (OFFICIAL STARTING POINT) Alignment and Structural Variant Calling: Tools such as Sniffles or SVIM detect large insertions, deletions, and other structural variants. 使用长读长测序工具如 SVIM 或 Sniffles 检测结构变异(e.g. 散在性重复序列)。

     #NOTE that the ./batch1_depth25/trycycler_WT/reads.fastq and F24A430001437_BACctmoD/BGI_result/Separate/${sample}/1.Cleandata/${sample}.filtered_reads.fq.gz are the same!
   
     # -- PREPARING the input fastq-data, merge the fastqz and move the top-directory
   
     # Under raw_data/no_sample_id/20250731_0943_MN45170_FBD12615_97f118c2/fastq_pass
     zcat ./barcode01/FBD12615_pass_barcode01_97f118c2_aa46ecf7_0.fastq.gz ./barcode01/FBD12615_pass_barcode01_97f118c2_aa46ecf7_1.fastq.gz ./barcode01/FBD12615_pass_barcode01_97f118c2_aa46ecf7_2.fastq.gz ./barcode01/FBD12615_pass_barcode01_97f118c2_aa46ecf7_3.fastq.gz ... | gzip > HD46_1.fastq.gz
     mv ./raw_data/no_sample_id/20250731_0943_MN45170_FBD12615_97f118c2/fastq_pass/HD46_1.fastq.gz ~/DATA/Data_Patricia_Transposon_2025
   
       #this are the corresponding sample names:
       #barcode 1: HD46-1
       #barcode 2: HD46-2
       #barcode 3: HD46-3
       #barcode 4: HD46-4
       mv barcode01.fastq.gz HD46_1.fastq.gz
       mv barcode02.fastq.gz HD46_2.fastq.gz
       mv barcode03.fastq.gz HD46_3.fastq.gz
       mv barcode04.fastq.gz HD46_4.fastq.gz
   
     # -- CALCULATE the coverages
       #!/bin/bash
   
       for bam in barcode*_minimap2.sorted.bam; do
           echo "Processing $bam ..."
           avg_cov=$(samtools depth -a "$bam" | awk '{sum+=$3; cnt++} END {if (cnt>0) print sum/cnt; else print 0}')
           echo -e "${bam}\t${avg_cov}" >> coverage_summary.txt
       done
   
     # ---- !!!! LOGIN the suitable environment !!!! ----
   
     mamba activate transposon_long
   
     # -- TODO: AFTERNOON_DEBUG_THIS: FAILED and not_USED: Alignment and Detect structural variants in each sample using SVIM which used aligner ngmlr or mimimap2
     #mamba install -c bioconda ngmlr
     mamba install -c bioconda svim
   
     # ---- Option_1: minimap2 (aligner) + SVIM (structural variant caller) --> SUCCESSFUL ----
     for sample in HD46_Ctrl HD46_1 HD46_2 HD46_3 HD46_4 HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
         #INS,INV,DUP:TANDEM,DUP:INT,BND
         svim reads --aligner minimap2 --nanopore minimap2+svim_${sample}    ${sample}.fastq.gz CP020463.fasta  --cores 20 --types INS --min_sv_size 100 --sequence_allele --insertion_sequences --read_names;
     done
   
     #svim alignment svim_alignment_minmap2_1_re 1.sorted.bam CP020463_.fasta --types INS --sequence_alleles --insertion_sequences --read_names
   
     # ---- Option_2: minamap2 (aligner) + Sniffles2 (structural variant caller) --> SUCCESSFUL ----
     #Minimap2: A commonly used aligner for nanopore sequencing data.
     #    Align Long Reads to the WT Reference using Minimap2
     #sniffles -m WT.sorted.bam -v WT.vcf -s 10 -l 50 -t 60
     #  -s 20: Requires at least 20 reads to support an SV for reporting. --> 10
     #  -l 50: Reports SVs that are at least 50 base pairs long.
     #  -t 60: Uses 60 threads for faster processing.
     for sample in HD46_Ctrl HD46_1 HD46_2 HD46_3 HD46_4 HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
         #minimap2 --MD -t 60 -ax map-ont CP020463.fasta ./batch1_depth25/trycycler_${sample}/reads.fastq | samtools sort -o ${sample}.sorted.bam
         minimap2 --MD -t 60 -ax map-ont CP020463.fasta ${sample}.fastq.gz | samtools sort -o ${sample}_minimap2.sorted.bam
         samtools index ${sample}_minimap2.sorted.bam
         sniffles -m ${sample}_minimap2.sorted.bam -v ${sample}_minimap2+sniffles.vcf -s 10 -l 50 -t 60
         #QUAL < 20 ||
         bcftools filter -e "INFO/SVTYPE != 'INS'" ${sample}_minimap2+sniffles.vcf > ${sample}_minimap2+sniffles_filtered.vcf
     done
   
       #Estimating parameter...
       #        Max dist between aln events: 44
       #        Max diff in window: 76
       #        Min score ratio: 2
       #        Avg DEL ratio: 0.0112045
       #        Avg INS ratio: 0.0364027
       #Start parsing... CP020463
       #                # Processed reads: 10000
       #                # Processed reads: 20000
       #        Finalizing  ..
       #Start genotype calling:
       #        Reopening Bam file for parsing coverage
       #        Finalizing  ..
       #Estimating parameter...
       #        Max dist between aln events: 28
       #        Max diff in window: 89
       #        Min score ratio: 2
       #        Avg DEL ratio: 0.013754
       #        Avg INS ratio: 0.17393
       #Start parsing... CP020463
       #                # Processed reads: 10000
       #                # Processed reads: 20000
       #                # Processed reads: 30000
       #                # Processed reads: 40000
   
       # Results:
       # * barcode01_minimap2+sniffles.vcf
       # * barcode01_minimap2+sniffles_filtered.vcf
       # * barcode02_minimap2+sniffles.vcf
       # * barcode02_minimap2+sniffles_filtered.vcf
       # * barcode03_minimap2+sniffles.vcf
       # * barcode03_minimap2+sniffles_filtered.vcf
       # * barcode04_minimap2+sniffles.vcf
       # * barcode04_minimap2+sniffles_filtered.vcf
   
     #ERROR: No MD string detected! Check bam file! Otherwise generate using e.g. samtools. --> No results!
     #for sample in barcode01 barcode02 barcode03 barcode04; do
     #    sniffles -m svim_reads_minimap2_${sample}/${sample}.fastq.minimap2.coordsorted.bam -v sniffles_minimap2_${sample}.vcf -s 10 -l 50 -t 60
     #    bcftools filter -e "INFO/SVTYPE != 'INS'" sniffles_minimap2_${sample}.vcf > sniffles_minimap2_${sample}_filtered.vcf
     #done
   
     # ---- Option_3: NGMLR (aligner) + SVIM (structural variant caller) --> SUCCESSFUL ----
     for sample in HD46_Ctrl HD46_1 HD46_2 HD46_3 HD46_4 HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
         svim reads --aligner ngmlr --nanopore    ngmlr+svim_${sample}       ${sample}.fastq.gz CP020463.fasta  --cores 10;
     done
   
     # ---- Option_4: NGMLR (aligner) + sniffles (structural variant caller) --> SUCCESSFUL ----
     for sample in HD46_Ctrl HD46_1 HD46_2 HD46_3 HD46_4 HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
         sniffles -m ngmlr+svim_${sample}/${sample}.fastq.ngmlr.coordsorted.bam -v ${sample}_ngmlr+sniffles.vcf -s 10 -l 50 -t 60
         bcftools filter -e "INFO/SVTYPE != 'INS'" ${sample}_ngmlr+sniffles.vcf > ${sample}_ngmlr+sniffles_filtered.vcf
     done

9. Compare and integrate all results produced by minimap2+sniffles and ngmlr+sniffles, and check them each position in IGV!

       mv HD46_Ctrl_minimap2+sniffles_filtered.vcf HD46-Ctrl_minimap2+sniffles_filtered.vcf
       mv HD46_Ctrl_ngmlr+sniffles_filtered.vcf    HD46-Ctrl_ngmlr+sniffles_filtered.vcf
       mv HD46_1_minimap2+sniffles_filtered.vcf    HD46-1_minimap2+sniffles_filtered.vcf
       mv HD46_1_ngmlr+sniffles_filtered.vcf       HD46-1_ngmlr+sniffles_filtered.vcf
       mv HD46_2_minimap2+sniffles_filtered.vcf    HD46-2_minimap2+sniffles_filtered.vcf
       mv HD46_2_ngmlr+sniffles_filtered.vcf       HD46-2_ngmlr+sniffles_filtered.vcf
       mv HD46_3_minimap2+sniffles_filtered.vcf    HD46-3_minimap2+sniffles_filtered.vcf
       mv HD46_3_ngmlr+sniffles_filtered.vcf       HD46-3_ngmlr+sniffles_filtered.vcf
       mv HD46_4_minimap2+sniffles_filtered.vcf    HD46-4_minimap2+sniffles_filtered.vcf
       mv HD46_4_ngmlr+sniffles_filtered.vcf       HD46-4_ngmlr+sniffles_filtered.vcf
       mv HD46_5_minimap2+sniffles_filtered.vcf    HD46-5_minimap2+sniffles_filtered.vcf
       mv HD46_5_ngmlr+sniffles_filtered.vcf       HD46-5_ngmlr+sniffles_filtered.vcf
       mv HD46_6_minimap2+sniffles_filtered.vcf    HD46-6_minimap2+sniffles_filtered.vcf
       mv HD46_6_ngmlr+sniffles_filtered.vcf       HD46-6_ngmlr+sniffles_filtered.vcf
       mv HD46_7_minimap2+sniffles_filtered.vcf    HD46-7_minimap2+sniffles_filtered.vcf
       mv HD46_7_ngmlr+sniffles_filtered.vcf       HD46-7_ngmlr+sniffles_filtered.vcf
       mv HD46_8_minimap2+sniffles_filtered.vcf    HD46-8_minimap2+sniffles_filtered.vcf
       mv HD46_8_ngmlr+sniffles_filtered.vcf       HD46-8_ngmlr+sniffles_filtered.vcf
       mv HD46_13_minimap2+sniffles_filtered.vcf   HD46-13_minimap2+sniffles_filtered.vcf
       mv HD46_13_ngmlr+sniffles_filtered.vcf      HD46-13_ngmlr+sniffles_filtered.vcf
   
     conda activate plot-numpy1
     #python generate_common_vcf.py
     #mv common_variants.xlsx putative_transposons.xlsx
   
     # * Reads each of your VCFs.
     # * Filters variants → only keep those with FILTER == PASS.
     # * Compares the two aligner methods (minimap2+sniffles2 vs ngmlr+sniffles2) per sample.
     # * Keeps only variants that appear in both methods for the same sample.
     # * Outputs: An Excel file with the common variants and a log text file listing which variants were filtered out, and why (not_PASS or not_COMMON_in_two_VCF).
   
     #python generate_fuzzy_common_vcf_v1.py
     #Sample   PASS_minimap2   PASS_ngmlr  COMMON
     #  HD46-Ctrl_Ctrl 39  29  28
     #  HD46-1 39  32  29
     #  HD46-2 40  32  28
     #  HD46-3 38  30  27
     #  HD46-4 46  35  32
     #  HD46-5 40  35  31
     #  HD46-6 43  35  30
     #  HD46-7 40  33  28
     #  HD46-8 37  20  11
     #  HD46-13    39  38  27
   
   #Sample PASS_minimap2   PASS_ngmlr  COMMON_FINAL
   #HD46-Ctrl_Ctrl 39  29  6
   #HD46-1 39  32  8
   #HD46-2 40  32  8
   #HD46-3 38  30  6
   #HD46-4 46  35  8
   #HD46-5 40  35  9
   #HD46-6 43  35  10
   #HD46-7 40  33  8
   #HD46-8 37  20  4
   #HD46-13    39  38  5
   
   #!!!! Summarize the results of ngmlr+sniffles !!!!
   python merge_ngmlr+sniffles_filtered_results_and_summarize.py
   
   #!!!! Post-Processing !!!!
   #DELETE "2186168    N   

   . PASS” in Sheet HD46-13 and Summary #DELETE “2427785 N CGTCAGAATCGCTGTCTGCGTCCGAGTCACTGTCTGAGTCTGAATCACTATCTGCGTCTGAGTCACTGTCTG . PASS” due to “0/1:169:117” in HD46-13 and Summary #DELETE “2441640 N GCTCATTAAGAATCATTAAATTAC . PASS” due to 0/1:170:152 in HD46-13 and Summary

10. Source code of merge_ngmlr+sniffles_filtered_results_and_summarize.py

    import os
    import pandas as pd
    
    # List all ngmlr VCF files
    vcf_files = sorted([f for f in os.listdir('.') if f.endswith('_ngmlr+sniffles_filtered.vcf')])
    
    log_lines = []
    df_list = []
    
    # Function to read VCF and filter PASS
    def read_vcf(vcf_path, log_lines, sample):
        variants = []
        with open(vcf_path) as f:
            for line in f:
                if line.startswith('#'):
                    continue
                parts = line.strip().split('\t')
                pos, ref, alt, qual, flt = parts[1], parts[3], parts[4], parts[5], parts[6]
                info = parts[7] if len(parts) > 7 else '.'
                fmt = parts[8] if len(parts) > 8 else '.'
                last_column = parts[9] if len(parts) > 9 else '.'  # Keep original column
                var_id = f"{pos}:{ref}>{alt}"
                if flt != 'PASS':
                    log_lines.append(f"{sample}\t{var_id}\tnot_PASS")
                    continue
                variants.append({
                    'POS': int(pos),
                    'REF': ref,
                    'ALT': alt,
                    'QUAL': qual,
                    'FILTER': flt,
                    'INFO': info,
                    'FORMAT': fmt,
                    sample: last_column,  # Use only genotype for individual sheets
                    f"{sample}_with_alt": alt  # Use only ALT sequence for summary
                })
        return pd.DataFrame(variants)
    
    # Read all VCFs
    sample_names = []
    sample_with_alt_columns = []
    for f in vcf_files:
        sample = os.path.basename(f).replace('_ngmlr+sniffles_filtered.vcf', '')
        sample_names.append(sample)
        sample_with_alt_columns.append(f"{sample}_with_alt")
        df = read_vcf(f, log_lines, sample)
        df_list.append(df)
    
    # Merge all variants into one DataFrame
    all_variants = pd.concat(df_list, ignore_index=True)
    
    # Fill missing sample columns with hyphen for summary
    for sample in sample_names:
        if sample not in all_variants.columns:
            all_variants[sample] = ''
        if f"{sample}_with_alt" not in all_variants.columns:
            all_variants[f"{sample}_with_alt"] = '-'
    
    # Pivot table for summary using exact POS, using columns with ALT
    summary_df = all_variants.groupby(['POS', 'REF'])[sample_with_alt_columns].first().reset_index()
    
    # Rename columns in summary to remove '_with_alt' suffix
    summary_df.columns = ['POS', 'REF'] + sample_names
    
    # Replace NaN or empty strings with hyphen in sample columns
    summary_df[sample_names] = summary_df[sample_names].fillna('-').replace('', '-')
    
    # Count how many samples have a non-hyphen value
    summary_df['Count'] = summary_df[sample_names].apply(lambda row: sum(val != '-' for val in row), axis=1)
    
    # Save Excel with individual sheets and summary
    writer = pd.ExcelWriter('merged_ngmlr+sniffles_variants.xlsx', engine='openpyxl')
    
    # Save individual sample sheets
    for df in df_list:
        sheet_name = df.columns[-2]  # Use sample name (not the _with_alt column)
        # Select only relevant columns for individual sheets (exclude _with_alt)
        df = df[[col for col in df.columns if not col.endswith('_with_alt')]]
        df.to_excel(writer, sheet_name=sheet_name, index=False)
    
    # Save summary sheet
    summary_df.to_excel(writer, sheet_name='Summary', index=False)
    writer.close()
    
    # Write log
    with open('ngmlr_filtering_log.txt', 'w') as logf:
        logf.write('Sample\tVariant\tReason\n')
        for line in log_lines:
            logf.write(line + '\n')
    
    print('Done: merged_ngmlr+sniffles_variants.xlsx with ALT sequences or hyphens in summary and genotype-only in individual sheets.')

11. ———————– END of the transposon calculation. The following steps (e.g. assembly based on the long-reads is not necessary for the transposon analysis) —————————-

12. (NOT_USED) Filtering low-complexity insertions using RepeatMasker (TODO: how to use RepeatModeler to generate own lib?)

      python vcf_to_fasta.py variants.vcf variants.fasta
      #python filter_low_complexity.py variants.fasta filtered_variants.fasta retained_variants.fasta
      #Using RepeatMasker to filter the low-complexity fasta, the used h5 lib is
      /home/jhuang/mambaforge/envs/transposon_long/share/RepeatMasker/Libraries/Dfam.h5    #1.9G
      python /home/jhuang/mambaforge/envs/transposon_long/share/RepeatMasker/famdb.py -i /home/jhuang/mambaforge/envs/transposon_long/share/RepeatMasker/Libraries/Dfam.h5 names 'bacteria' | head
      Exact Matches
      =============
      2 bacteria (blast name), Bacteria 

    (scientific name), eubacteria (genbank common name), Monera (in-part), Procaryotae (in-part), Prokaryota (in-part), Prokaryotae (in-part), prokaryote (in-part), prokaryotes (in-part) Non-exact Matches ================= 1783272 Terrabacteria group (scientific name) 91061 Bacilli (scientific name), Bacilli Ludwig et al. 2010 (authority), Bacillus/Lactobacillus/Streptococcus group (synonym), Firmibacteria (synonym), Firmibacteria Murray 1988 (authority) 1239 Bacillaeota (synonym), Bacillaeota Oren et al. 2015 (authority), Bacillota (synonym), Bacillus/Clostridium group (synonym), clostridial firmicutes (synonym), Clostridium group firmicutes (synonym), Firmacutes (synonym), firmicutes (blast name), Firmicutes (scientific name), Firmicutes corrig. Gibbons and Murray 1978 (authority), Low G+C firmicutes (synonym), low G+C Gram-positive bacteria (common name), low GC Gram+ (common name) Summary of Classes within Firmicutes: * Bacilli (includes many common pathogenic and non-pathogenic Gram-positive bacteria, taxid=91061) * Bacillus (e.g., Bacillus subtilis, Bacillus anthracis) * Staphylococcus (e.g., Staphylococcus aureus, Staphylococcus epidermidis) * Streptococcus (e.g., Streptococcus pneumoniae, Streptococcus pyogenes) * Listeria (e.g., Listeria monocytogenes) * Clostridia (includes many anaerobic species like Clostridium and Clostridioides) * Erysipelotrichia (intestinal bacteria, some pathogenic) * Tissierellia (less-studied, veterinary relevance) * Mollicutes (cell wall-less, includes Mycoplasma species) * Negativicutes (includes some Gram-negative, anaerobic species) RepeatMasker -species Bacilli -pa 4 -xsmall variants.fasta python extract_unmasked_seq.py variants.fasta.masked unmasked_variants.fasta #bcftools filter -i ‘QUAL>30 && INFO/SVLEN>100’ variants.vcf -o filtered.vcf # #bcftools view -i ‘SVTYPE=”INS”‘ variants.vcf | bcftools query -f ‘%CHROM\t%POS\t%REF\t%ALT\t%INFO\n’ > insertions.txt #mamba install -c bioconda vcf2fasta #vcf2fasta variants.vcf -o insertions.fasta #grep “SEQS” variants.vcf | awk ‘{ print $1, $2, $4, $5, $8 }’ > insertions.txt #python3 filtering_low_complexity.py # #vcftools –vcf input.vcf –recode –out filtered_output –minSVLEN 100 #bcftools filter -e ‘INFO/SEQS ~ “^(G+|C+|T+|A+){4,}”‘ variants.vcf -o filtered.vcf # — calculate the percentage of reads To calculate the percentage of reads that contain the insertion from the VCF entry, use the INFO and FORMAT fields provided in the VCF record. Step 1: Extract Relevant Information In the provided VCF entry: RE (Reads Evidence): 733 – the total number of reads supporting the insertion. GT (Genotype): 1/1 – this indicates a homozygous insertion, meaning all reads covering this region are expected to have the insertion. AF (Allele Frequency): 1 – a 100% allele frequency, indicating that every read in this sample supports the insertion. DR (Depth Reference): 0 – the number of reads supporting the reference allele. DV (Depth Variant): 733 – the number of reads supporting the variant allele (insertion). Step 2: Calculate Percentage of Reads Supporting the Insertion Using the formula: Percentage of reads with insertion=(DVDR+DV)×100 Percentage of reads with insertion=(DR+DVDV​)×100 Substitute the values: Percentage=(7330+733)×100=100% Percentage=(0+733733​)×100=100% Conclusion Based on the VCF record, 100% of the reads support the insertion, indicating that the insertion is fully present in the sample (homozygous insertion). This is consistent with the AF=1 and GT=1/1 fields. * In your VCF file generated by Sniffles, the REF=N in the results has a specific meaning: * In a standard VCF, the REF field usually contains the reference base(s) at the variant position. * For structural variants (SVs), especially insertions, there is no reference sequence replaced; the insertion occurs between reference bases. * Therefore, Sniffles uses N as a placeholder in the REF field to indicate “no reference base replaced”. * The actual inserted sequence is then stored in the ALT field.

13. Why some records have UNRESOLVED in the FILTER field in the Excel output.

    1. Understanding the format
    
        The data appears to be structural variant (SV) calls from Sniffles, probably in a VCF-like tabular format exported to Excel:
    
            * gi|1176884116|gb|CP020463.1| → reference sequence
            * Positions: 1855752 and 2422820
            * N → insertion event
            * SVLEN=999 → size of the insertion
            * AF → allele frequency
            * GT:DR:DV → genotype, depth reference, depth variant (1/1:0:678, example values for a PASS variant)
            * FILTER → whether the variant passed filters (UNRESOLVED means it didn’t pass)
    
    2. What UNRESOLVED usually means
    
        In Sniffles:
    
        * UNRESOLVED is assigned to SVs when the tool cannot confidently resolve the exact sequence or breakpoint.
        * Reasons include:
            - Low read support (RE, DV) relative to the expected coverage
            - Ambiguous alignment at repetitive regions
            - Conflicting strand or orientation signals
            - Allele frequency inconsistent with expectations
    
    3. Examine your two records
    
        First record
    
            POS: 1855752
            SVTYPE: INS
            SVLEN: 999
            RE: 68
            AF: 1
            GT: 1/1
            FILTER: UNRESOLVED
    
        Observations:
    
        * AF = 1 → allele frequency 100%, homozygous insertion
        * RE = 68 → 68 reads support the variant, decent coverage
        * Still UNRESOLVED → likely because Sniffles could not resolve the inserted sequence precisely; sometimes long insertions in repetitive regions are hard to reconstruct fully even with good read support.
    
        Second record
    
            POS: 2422820
            SVTYPE: INS
            SVLEN: 999
            RE: 22
            AF: 0.025522
            GT: 0/0
            FILTER: UNRESOLVED
    
        Observations:
    
        * AF = 0.0255 → very low allele frequency (~2.5%)
        * RE = 22, DR = 840 → very low variant reads vs reference
        * GT = 0/0 → homozygous reference
        * Sniffles marks it UNRESOLVED because the variant is essentially noise, not confidently detected.
    
    4. Key difference between the two
        Feature First record    Second record
        Allele frequency (AF)   1 (high)    0.0255 (very low)
        Variant reads (RE)  68  22
        Genotype (GT)   1/1 0/0
        Reason for UNRESOLVED   Unresolvable inserted sequence
    
    ✅ 5. Conclusion
    
        * Sniffles marks a variant as UNRESOLVED when the SV cannot be confidently characterized.
        * Even if there is good read support (first record), complex insertions can’t always be reconstructed fully.
        * Very low allele frequency (second record) also triggers UNRESOLVED because the signal is too weak compared to background noise.
        * Essentially: “UNRESOLVED” ≠ bad data, it’s just unresolved uncertainty.

14. (NOT_SURE_HOW_TO_USE) Polishing of assembly: Use tools like Medaka to refine variant calls by leveraging consensus sequences derived from nanopore data.

      mamba install -c bioconda medaka
      medaka-consensus -i aligned_reads.bam -r reference.fasta -o polished_output -t 4

15. Compare Insertions Across Samples

    Merge Variants Across Samples: Use SURVIVOR to merge and compare the detected insertions in all samples against the WT:
    
    SURVIVOR merge input_vcfs.txt 1000 1 1 1 0 30 merged.vcf
    
        Input: List of VCF files from Sniffles2.
        Output: A consolidated VCF file with shared and unique variants.
    
    Filter WT Insertions:
    
        Identify transposons present only in samples 1–9 by subtracting WT variants using bcftools:
    
            bcftools isec WT.vcf merged.vcf -p comparison_results

16. Validate and Visualize

    Visualize with IGV: Use IGV to inspect insertion sites in the alignment and confirm quality.
    
    igv.sh
    
    Validate Findings:
        Perform PCR or additional sequencing for key transposon insertion sites to confirm results.

17. Alternatives to TEPID for Long-Read Data

    If you’re looking for transposon-specific tools for long reads:
    
        REPET: A robust transposon annotation tool compatible with assembled genomes.
        EDTA (Extensive de novo TE Annotator):
            A pipeline to identify, classify, and annotate transposons.
            Works directly on your assembled genomes.
    
            perl EDTA.pl --genome WT.fasta --type all

18. The WT.vcf file in the pipeline is generated by detecting structural variants (SVs) in the wild-type (WT) genome aligned against itself or using it as a baseline reference. Here’s how you can generate the WT.vcf:

    Steps to Generate WT.vcf
    1. Align WT Reads to the WT Reference Genome
    
    The goal here is to create an alignment of the WT sequencing data to the WT reference genome to detect any self-contained structural variations, such as native insertions, deletions, or duplications.
    
    Command using Minimap2:
    
    minimap2 -ax map-ont WT.fasta WT_reads.fastq | samtools sort -o WT.sorted.bam
    
    Index the BAM file:
    
    samtools index WT.sorted.bam
    
    2. Detect Structural Variants with Sniffles2
    
    Run Sniffles2 on the WT alignment to call structural variants:
    
    sniffles --input WT.sorted.bam --vcf WT.vcf
    
    This step identifies:
    
        Native transposons and insertions present in the WT genome.
        Other structural variants that are part of the reference genome or sequencing artifacts.
    
    Key parameters to consider:
    
        --min_support: Adjust based on your WT sequencing coverage.
        --max_distance: Define proximity for merging variants.
        --min_length: Set a minimum SV size (e.g., >50 bp for transposons).

19. Clean and Filter the WT.vcf, Variant Filtering: Remove low-confidence variants based on read depth, quality scores, or allele frequency.

    To ensure the WT.vcf only includes relevant transposons or SVs:
    
        Use bcftools or similar tools to filter out low-confidence variants:
    
        bcftools filter -e "QUAL < 20 || INFO/SVTYPE != 'INS'" WT.vcf > WT_filtered.vcf
        bcftools filter -e "QUAL < 1 || INFO/SVTYPE != 'INS'" 1_.vcf > 1_filtered_.vcf

20. NOTE that in this pipeline, the WT.fasta (reference genome) is typically a high-quality genome sequence from a database or a well-annotated version of your species’ genome. It is not assembled from the WT.fastq sequencing reads in this context. Here’s why:

    Why Use a Reference Genome (WT.fasta) from a Database?
    
        Higher Quality and Completeness:
            Database references (e.g., NCBI, Ensembl) are typically well-assembled, highly polished, and annotated. They serve as a reliable baseline for variant detection.
    
        Consistency:
            Using a standard reference ensures consistent comparisons across your WT and samples (1–9). Variants detected will be relative to this reference, not influenced by possible assembly errors.
    
        Saves Time:
            Assembling a reference genome from WT reads requires significant computational effort. Using an existing reference streamlines the analysis.
    
    Alternative: Assembling WT from FASTQ
    
    If you don’t have a high-quality reference genome (WT.fasta) and must rely on your WT FASTQ reads:
    
        Assemble the genome from your WT.fastq:
            Use long-read assemblers like Flye, Canu, or Shasta to create a draft genome.
    
        flye --nano-raw WT.fastq --out-dir WT_assembly --genome-size 

    Polish the assembly using tools like Racon (with the same reads) or Medaka for higher accuracy. Use the assembled and polished genome as your WT.fasta reference for further steps. Key Takeaways: If you have access to a reliable, high-quality reference genome, use it as the WT.fasta. Only assemble WT.fasta from raw reads (WT.fastq) if no database reference is available for your organism.

21. Annotate Transposable Elements: Tools like ANNOVAR or SnpEff provide functional insights into the detected variants.

    # -- (successful!) MANUALLY Search for all found insertion sequences at https://tncentral.ncc.unesp.br/blast/ !
    # Or use the program available at https://github.com/danillo-alvarenga/tncomp_finder if there are numerous matches.
    #https://tncentral.ncc.unesp.br/report/te/Tn551-Y13600.1
    
    # -- (failed!) try TEPID for annotation
    mamba install tepid=0.10 -c bioconda
    #(tepid_env)
    for sample in WT 1 2 3 4 5 7 8 9 10; do
        tepid-map-se -x CP020463 -p 10 -n ${sample}_tepid -q  ../batch1_depth25/trycycler_${sample}/reads.fastq;
        tepid-discover -k -i -p 10 -n ${sample}_tepid -c ${sample}_tepid --se;
    done
    
    tepid-discover -k -i -p 10 -n 1_tepid -c 1.sorted.bam --se;
    
    tepid-refine [-h] [--version] [-k] [-i INSERTIONS] [-d DELETIONS]
                [-p PROC] -t TE -n NAME -c CONC -s SPLIT -a AL
    
    # -- (failed!) try EDTA for annotation
    https://github.com/oushujun/EDTA
    (transposon_long) mamba install -c conda-forge -c bioconda edta
    mamba install bioconda::rmblast  # cd RepeatMasker; ./configure
    EDTA.pl --genome CP020463.fasta --species others --threads 40
    
    For general-purpose TE annotation: EDTA, RepeatMasker, or RepeatScout are your best options.
    For de novo repeat identification: RepeatScout is highly effective.
    For LTR retrotransposons: Use LTR_retriever.
    For bacterial-specific annotations: Transposome, TEfinder, and ISfinder can be useful.

22. Validation: Cross-validate with short-read sequencing data if available.

23. (Optional) Assembly the nanopore-sequencing using

    1. merge and prepare fastq.gz
    
    2. calcuclate the precentage of coding DNA: https://www.ncbi.nlm.nih.gov/nuccore/CP020463 with additional plasmid.
    
        * Average gene length: 870.4 bp
        * Total coding region length: 2056168 bp
        * Percentage of coding DNA = (Total Coding Region Length / Total Genome Length) × 100 = 2056168 bp / 2454929 bp = 83.8%
    
    3. Prepare the fastq.gz
    
        conda activate /home/jhuang/miniconda3/envs/trycycler  # under jhuang@hamm (10.169.63.113).
    
        rsync -a -P *.fastq.gz jhuang@10.169.63.113:/home/jhuang/DATA/Data_Patricia_Transposon_2025/
        for sample in barcode01 barcode02 barcode03 barcode04  HD46_Ctrl HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
            mkdir trycycler_${sample};
            mv ${sample}.fastq.gz trycycler_${sample}/reads.fastq.gz;
            gunzip trycycler_${sample}/reads.fastq.gz;
        done
        #mkdir batch3;
        #cd batch3;
        #for sample in WT 1 2 3 4 5 7 8 9 10; do
        #    mkdir trycycler_${sample};
        #    cp ${sample}_nanopore/${sample}.fastq.gz trycycler_${sample}/reads.fastq.gz;
        #    gunzip trycycler_${sample}/reads.fastq.gz;
        #done
    
    4. Running separate assemblies (6x4 times: canu, flye, miniasm_and_minipolish, necat, nextdenovo, raven) using trycycler_assembly_extra-thorough.sh (under the trycycler environment running the following steps)
            # batch1: min_read_cov=25; batch2: min_read_cov=50; batch3: min_read_cov=100 (if necessary on the monday!)
            cp ../Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_*.sh ./
            #for sample in trycycler_HDRNA_10 trycycler_HDRNA_13; do
            for sample in barcode01 barcode02 barcode03 barcode04  HD46_Ctrl HD46_5 HD46_6 HD46_7 HD46_8 HD46_13; do
                cd trycycler_${sample};
                ../trycycler_assembly_extra-thorough.sh
                cd ..;
            done
    
    # END!
    #TODO: upload the nanopore-sequencing data to NCBI for the HDRNA_10 and HDRNA_13 to the correspoinding project.
    
    (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_10/assemblies$ mlst assembly_02.fasta
    assembly_02.fasta       sepidermidis    87      arcC(7) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    [15:32:46] If you like MLST, you're absolutely going to love wgMLST!
    [15:32:46] Done.
    (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_10/assemblies$ mlst assembly_20.fasta
    
    assembly_20.fasta       sepidermidis    87      arcC(7) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    [15:33:01] You can use --label XXX to replace an ugly filename in the output.
    [15:33:01] Done.
    
    (bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_13/assemblies$ mlst assembly_02.fasta
    assembly_02.fasta       sepidermidis    5       arcC(1) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    
    (bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_13/assemblies$ mlst assembly_08.fasta
    
    assembly_08.fasta       sepidermidis    5       arcC(1) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    [15:38:22] Remember that --minscore is only used when using automatic scheme detection.
    [15:38:22] Done.
    (bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_13/assemblies$ mlst assembly_14.fasta
    assembly_14.fasta       sepidermidis    5       arcC(1) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    [15:38:50] Thanks for using mlst, I hope you found it useful.
    [15:38:50] Done.
    (bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_PaulBongarts_S.epidermidis_HDRNA/trycycler_HDRNA_13/assemblies$ mlst assembly_20.fasta
    assembly_20.fasta       sepidermidis    5       arcC(1) aroE(1) gtr(1)  mutS(2) pyrR(2) tpiA(1) yqiL(1)
    [15:38:54] If you like MLST, you're going to absolutely love cgMLST!
    [15:38:54] Done.
    
            #- under the directory batch3
            #for sample in trycycler_WT trycycler_1 trycycler_2 trycycler_3 trycycler_4 trycycler_5 trycycler_7 trycycler_8 trycycler_9 trycycler_10; do
            #    cd ${sample};
            #    ../trycycler_assembly_extra-thorough_threads5_cov100.sh;
            #    cd ..;
            #done
    
            #if ERROR, running separate assembly-methods raven and canu
            #for sample in trycycler_WT trycycler_1 trycycler_2 trycycler_3 trycycler_4 trycycler_5 trycycler_7 trycycler_8 trycycler_9 trycycler_10; do
            #    cd ${sample};
            #    ../trycycler_assembly_extra-thorough_raven.sh;
            #    cd ..;
            #done
            #
            #for sample in trycycler_WT trycycler_1 trycycler_2 trycycler_3 trycycler_4 trycycler_5 trycycler_7 trycycler_8 trycycler_9 trycycler_10; do
            #    cd ${sample};
            #    ../trycycler_assembly_extra-thorough_canu.sh;
            #    cd ..;
            #done
    
    5. trycycler cluster
    
            for sample in trycycler_5 trycycler_7 trycycler_8 trycycler_9 trycycler_10; do
            for sample in trycycler_WT trycycler_1 trycycler_2 trycycler_3 trycycler_4; do
                cd ${sample};
                rm -rf trycycler
                trycycler cluster --threads 10 --assemblies assemblies/*.fasta --reads reads.fastq --out_dir trycycler;
                cd ..;
            done
    
    6. trycycler reconcile
    
            cd trycycler_WT
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/C_utg000001c.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/K_ctg000000.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/S_tig00000001.fasta trycycler/cluster_001/1_contigs_removed
    
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
                # -- under batch3 --
                176K Nov  5 12:38 P_bctg00000001.fasta
                122K Nov  5 12:38 D_bctg00000001.fasta
                95K Nov  5 12:38 J_bctg00000001.fasta
                78K Nov  5 12:38 J_bctg00000002.fasta
                #--> Error: unable to find a suitable common sequence
    
                69K Nov  5 17:24 G_tig00000004.fasta
                66K Nov  5 17:24 S_tig00000004.fasta
                66K Nov  5 17:24 M_tig00000004.fasta
                65K Nov  5 17:24 A_tig00000005.fasta
                #--> Error: unable to find a suitable common sequence
                #If repeat M_tig00000004.fasta twice, resulting in the following error: Circularising M_tig00000004:
                #  using S_tig00000004:
                #    unable to circularise: M_tig00000004's start and end were found in multiple places in S_tig00000004
                #Error: failed to circularise sequence M_tig00000004 because its start/end sequences were found in multiple ambiguous places in other sequences. #This is likely because M_tig00000004 starts/ends in a repetitive region. You can either manually repair its circularisation (and ensure it does #not start/end in a repetitive region) or exclude the sequence altogether and try again.
    
                47K Nov  5 17:24 V_bctg00000001.fasta
                45K Nov  5 17:24 C_utg000002c.fasta
                #Error: unable to find a suitable common sequence
    
                34K Nov  5 17:24 U_utg000004c.fasta
                34K Nov  5 17:24 N_contig_2.fasta
                34K Nov  5 17:24 T_contig_2.fasta
                #Error: unable to find a suitable common sequence
    
                23K Nov  5 17:24 I_utg000003c.fasta
                22K Nov  5 17:24 B_contig_2.fasta
                22K Nov  5 17:24 H_contig_2.fasta
                #Error: unable to find a suitable common sequence
    
                13K Nov  5 17:24 G_tig00000006.fasta
                12K Nov  5 17:24 M_tig00000003.fasta
                12K Nov  5 17:24 O_utg000002c.fasta
                12K Nov  5 17:24 S_tig00000003.fasta
                11K Nov  5 17:24 A_tig00000004.fasta
                #trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002 --max_length_diff 1.2 --max_trim_seq_percent 20.0
                #--> SUCCESSFULLY saving sequences to file: trycycler/cluster_002/2_all_seqs.fasta
    
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004 --max_length_diff 1.3 --max_trim_seq_percent 30.0
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_005 #Error: two or more input contigs are required
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_006 #Error: two or more input contigs are required
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_007 #Error: two or more input contigs are required
    
            cd ../trycycler_1
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/I_utg000001l.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/K_ctg000000.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/S_tig00000001.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/W_ctg000000.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/A_tig00000001.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/U_utg000001c.fasta trycycler/cluster_001/1_contigs_removed
    
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            #TODO_TOMORROW_HERE!
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003 --max_length_diff 1.7 --max_trim_seq_percent 70.0
            #Error: failed to circularise sequence I_utg000002l because its end could not be found in other sequences.
            (NOTE) using the plasmid from other isolate to help the cut of the sequence.
                #cp ~/DATA/Data_Patricia_Transposon/batch3/trycycler_WT/trycycler/cluster_004/7_final_consensus.fasta .
                #cat *.fasta > all.fasta
                #mafft --clustalout --adjustdirection all.fasta > all.aln
            cluster_004_con -------------------------------------------------------tatga
            I_utg000002l    agaatcagaattaggcgcataatttacaggaggtttgattatggctaagaaaaaatatga
            O_utg000002l    agaatcagaattaggcgcataatttacaggaggtttgattatggctaagaaaaaatatga
                                                                                *****
            cluster_004_con gcagaatcagaattaggcgcataatttacaggaggtttgattatggctaagaaaaaa---
            I_utg000002l    gcagaatcagaattaggcgcataatttacaggaggtttgattatggctaagaaaaatatg
            O_utg000002l    gcagaatcagaattaggcgcataatttacaggaggtttgattatggctaagaaaaatatg
                            ********************************************************
    
            grep "I_utg000002l" all.a_ > I_utg000002l.fasta
            grep "O_utg000002l" all.a_ > O_utg000002l.fasta
            cut -d' ' -f5 I_utg000002l.fasta > I_utg000002l_.fasta
            cut -d' ' -f5 O_utg000002l.fasta > O_utg000002l_.fasta
            sed 's/-//g' I_utg000002l_.fasta > I_utg000002l__.fasta
            sed 's/-//g' O_utg000002l_.fasta > O_utg000002l__.fasta
            seqtk seq I_utg000002l__.fasta -l 80 > I_utg000002l___.fasta
            seqtk seq O_utg000002l__.fasta -l 80 > O_utg000002l___.fasta
    
            #Cut two files Manually with the sequence: AGAATCAGAATTAGGCGCATAATTTACAGGAGGTTTGATTATGGCTAAGAAAAA
            cat I_utg000002l___.fasta O_utg000002l___.fasta > 2_all_seqs.fasta
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004 #Error: two or more input contigs are required
    
            cd ../trycycler_2
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/O_bctg00000000.fasta trycycler/cluster_001/1_contigs_removed
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
    
            cd ../trycycler_3
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/E_ctg000000.fasta trycycler/cluster_001/1_contigs_removed
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
    
            cd ../trycycler_4
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/C_utg000001l.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/E_ctg000000.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/F_Utg670.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/V_bctg00000000.fasta trycycler/cluster_001/1_contigs_removed
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_004
    
            cd ../trycycler_5
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_001
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 5 --reads reads.fastq --cluster_dir trycycler/cluster_004
    
            cd ../trycycler_7
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/C_utg000001l.fasta trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/D_bctg00000000.fasta trycycler/cluster_001/1_contigs_removed
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_005
    
            cd ../trycycler_8
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            mkdir trycycler/cluster_001/1_contigs_removed
            mv trycycler/cluster_001/1_contigs/M_utg000001l.fasta trycycler/cluster_001/1_contigs_removed
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_005
    
            cd ../trycycler_9
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_005
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_006
    
            cd ../trycycler_10
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_001
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_002
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_003
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_004
            trycycler reconcile --threads 55 --reads reads.fastq --cluster_dir trycycler/cluster_005
    
            #Nachmachen: for sample in trycycler_9 trycycler_10; do
            cd ${sample};
            ../trycycler_assembly_extra-thorough_canu.sh;
            cd ..;
            done
    
    7. (optional) map the circular assemblies to plasmid databases
            #bzip2 -d plsdb.fna.bz2
            #makeblastdb -in plsdb.fna -dbtype nucl
            #blastn -db plsdb.fna -query all.fasta -evalue 1e-50 -num_threads 15 -outfmt 6 -strand both -max_target_seqs 1 > all_on_plsdb.blastn
    
    8. trycycler msa
    
            trycycler msa --threads 55 --cluster_dir trycycler/cluster_001
            trycycler msa --threads 55 --cluster_dir trycycler/cluster_002
            trycycler msa --threads 55 --cluster_dir trycycler/cluster_003
            trycycler msa --threads 55 --cluster_dir trycycler/cluster_004
            trycycler msa --threads 55 --cluster_dir trycycler/cluster_005
            #--> When finished, Trycycler reconcile will make a 3_msa.fasta file in the cluster directory
    
    9. trycycler partition
    
            #generate 4_reads.fastq for each contig!
            trycycler partition --threads 55 --reads reads.fastq --cluster_dirs trycycler/cluster_*
            #trycycler partition --threads 55 --reads reads.fastq --cluster_dirs trycycler/cluster_001 trycycler/cluster_002 trycycler/cluster_003
            trycycler partition --threads 55 --reads reads.fastq --cluster_dirs trycycler/cluster_003
    
    10. trycycler consensus
    
            trycycler consensus --threads 55 --cluster_dir trycycler/cluster_001
            trycycler consensus --threads 55 --cluster_dir trycycler/cluster_002
            trycycler consensus --threads 55 --cluster_dir trycycler/cluster_003
            trycycler consensus --threads 55 --cluster_dir trycycler/cluster_004
            trycycler consensus --threads 55 --cluster_dir trycycler/cluster_005
            #!!NOTE that we take the isolates of HD05_2_K5 and HD05_2_K6 assembled by Unicycler instead of Trycycler!!
            # TODO (TODAY), generate the 3 datasets below!
            # TODO (IMPORTANT): write a Email to Holger, say the short sequencing of HD5_2 is not correct, since the 3 datasets! However, the MTxxxxxxx is confirmed not in K5 and K6!
            TODO: variant calling needs the short-sequencing, they are not dorable without the correct short-reads! resequencing? It is difficult to call variants only from long-reads since too much errors in long-reads!
            #TODO: check the MT sequence if in the isolates, more deteiled annotations come late!
            #II. Comparing the results of Trycycler with Unicycler.
            #III. Eventually add the plasmids assembled from unicycler to the final results. E.g. add the 4 plasmids to K5 and K6
    
    11. Polishing after Trycycler
    
            #1. Oxford Nanopore sequencer (Ignored due to the samtools version incompatibility!)
            # for c in trycycler/cluster_*; do
            #     medaka_consensus -i "$c"/4_reads.fastq -d "$c"/7_final_consensus.fasta -o "$c"/medaka -m r941_min_sup_g507 -t 12
            #     mv "$c"/medaka/consensus.fasta "$c"/8_medaka.fasta
            #     rm -r "$c"/medaka "$c"/*.fai "$c"/*.mmi  # clean up
            # done
            # cat trycycler/cluster_*/8_medaka.fasta > trycycler/consensus.fasta
            #2. Short-read polishing
            #---- 5179_R1 (2) ----
            #  mean read depth: 205.8x
            #  188 bp have a depth of zero (99.9924% coverage)
            #  355 positions changed (0.0144% of total positions)
            #  estimated pre-polishing sequence accuracy: 99.9856% (Q38.42)
            #Step 1: read QC
            fastp --in1 ../../s-epidermidis-5179-r1_R1.fastq.gz --in2 ../../s-epidermidis-5179-r1_R2.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../s-epidermidis-5179-r1_R1.fastq.gz ../../../s-epidermidis-5179-r1_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 37
            #Insertion/Deletion Errors: 2
            #Assembly Size: 2470001
            #Consensus Quality: 99.9984
            #Substitution Errors: 4
            #Insertion/Deletion Errors: 0
            #Assembly Size: 17748
            #Consensus Quality: 99.9775
            #Step 4: (optional) more rounds and/or other polishers
            #After one round of Polypolish and one round of POLCA, your assembly should be in very good shape!
            #However, there may still be a few lingering errors. You can try running additional rounds of Polypolish or POLCA to see if they make any more changes.
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../s-epidermidis-5179-r1_R1.fastq.gz ../../../s-epidermidis-5179-r1_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            Substitution Errors: 13
            Insertion/Deletion Errors: 0
            Assembly Size: 2470004
            Consensus Quality: 99.9995
            Substitution Errors: 0
            Insertion/Deletion Errors: 0
            Assembly Size: 17748
            Consensus Quality: 100
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../s-epidermidis-5179-r1_R1.fastq.gz ../../../s-epidermidis-5179-r1_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2470004
            #Consensus Quality: 100
            #---- 1585 (4) ----
            #  mean read depth: 174.7x
            #  8,297 bp have a depth of zero (99.6604% coverage)
            #  271 positions changed (0.0111% of total positions)
            #  estimated pre-polishing sequence accuracy: 99.9889% (Q39.55)
            #Step 1: read QC
            fastp --in1 ../../s-epidermidis-1585_R1.fastq.gz --in2 ../../s-epidermidis-1585_R2.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../s-epidermidis-1585_R1.fastq.gz ../../../s-epidermidis-1585_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 7
            #Insertion/Deletion Errors: 4
            #Assembly Size: 2443174
            #Consensus Quality: 99.9995
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 9014
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 9014
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2344
            #Consensus Quality: 100
            #Step 4: (optional) more rounds and/or other polishers
            #After one round of Polypolish and one round of POLCA, your assembly should be in very good shape!
            #However, there may still be a few lingering errors. You can try running additional rounds of Polypolish or POLCA to see if they make any more changes.
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../s-epidermidis-1585_R1.fastq.gz ../../../s-epidermidis-1585_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2443176
            #Consensus Quality: 100
            #---- 1585 derived from unicycler, under 1585_normal/unicycler (4) ----
            #Step 0: copy chrom and plasmid1, plasmid2, plasmid3 to cluster_001/7_final_consensus.fasta, ...
            #Step 1: read QC
            fastp --in1 ../../s-epidermidis-1585_R1.fastq.gz --in2 ../../s-epidermidis-1585_R2.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Polishing 1 (2,443,574 bp):
            #mean read depth: 174.7x
            #8,298 bp have a depth of zero (99.6604% coverage)
            #52 positions changed (0.0021% of total positions)
            #estimated pre-polishing sequence accuracy: 99.9979% (Q46.72)
            #Polishing 2 (9,014 bp):
            #mean read depth: 766.5x
            #3 bp have a depth of zero (99.9667% coverage)
            #0 positions changed (0.0000% of total positions)
            #estimated pre-polishing sequence accuracy: 100.0000% (Q∞)
            #Polishing 7 (2,344 bp):
            #mean read depth: 2893.0x
            #4 bp have a depth of zero (99.8294% coverage)
            #0 positions changed (0.0000% of total positions)
            #estimated pre-polishing sequence accuracy: 100.0000% (Q∞)
            #Polishing 8 (2,255 bp):
            #mean read depth: 2719.6x
            #4 bp have a depth of zero (99.8226% coverage)
            #0 positions changed (0.0000% of total positions)
            #estimated pre-polishing sequence accuracy: 100.0000% (Q∞)
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../s-epidermidis-1585_R1.fastq.gz ../../../s-epidermidis-1585_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 7
            #Insertion/Deletion Errors: 4
            #Assembly Size: 2443598
            #Consensus Quality: 99.9995
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 9014
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2344
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2255
            #Consensus Quality: 100
            #Step 4: (optional) more rounds and/or other polishers
            #After one round of Polypolish and one round of POLCA, your assembly should be in very good shape!
            #However, there may still be a few lingering errors. You can try running additional rounds of Polypolish or POLCA to see if they make any more changes.
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../s-epidermidis-1585_R1.fastq.gz ../../../s-epidermidis-1585_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2443600
            #Consensus Quality: 100
            #-- 1585v (1, no short reads, waiting) --
            # TODO!
            #-- 5179 (2) --
            #mean read depth: 120.7x
            #7,547 bp have a depth of zero (99.6946% coverage)
            #356 positions changed (0.0144% of total positions)
            #estimated pre-polishing sequence accuracy: 99.9856% (Q38.41)
            #Step 1: read QC
            fastp --in1 ../../s-epidermidis-5179_R1.fastq.gz --in2 ../../s-epidermidis-5179_R2.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../s-epidermidis-5179_R1.fastq.gz ../../../s-epidermidis-5179_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 49
            #Insertion/Deletion Errors: 23
            #Assembly Size: 2471418
            #Consensus Quality: 99.9971
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 17748
            #Consensus Quality: 100
            #Step 4: (optional) more rounds POLCA
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../s-epidermidis-5179_R1.fastq.gz ../../../s-epidermidis-5179_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 10
            #Insertion/Deletion Errors: 5
            #Assembly Size: 2471442
            #Consensus Quality: 99.9994
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../s-epidermidis-5179_R1.fastq.gz ../../../s-epidermidis-5179_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            Substitution Errors: 6
            Insertion/Deletion Errors: 0
            Assembly Size: 2471445
            Consensus Quality: 99.9998
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../s-epidermidis-5179_R1.fastq.gz ../../../s-epidermidis-5179_R2.fastq.gz" -t 55 -m 120G
            cd ..
            done
            Substitution Errors: 0
            Insertion/Deletion Errors: 0
            Assembly Size: 2471445
            Consensus Quality: 100
            #-- HD5_2 (2): without the short-sequencing we cannot correct the base-calling! --
            # !ERROR to be REPORTED, the
            #Polishing cluster_001_consensus (2,504,140 bp):
            #mean read depth: 94.4x
            #240,420 bp have a depth of zero (90.3991% coverage)
            #56,894 positions changed (2.2720% of total positions)
            #estimated pre-polishing sequence accuracy: 97.7280% (Q16.44)
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_1_S37_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_1_S37_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_2_S38_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_2_S38_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_3_S39_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_3_S39_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_4_S40_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_4_S40_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_5_S41_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_5_S41_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_6_S42_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_6_S42_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_7_S43_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_7_S43_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_8_S44_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_8_S44_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_9_S45_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_9_S45_R2_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_10_S46_R1_001.fastq
            /media/jhuang/Elements2/Data_Anna12_HAPDICS_HyAsP/180821_rohde/HD5_10_S46_R2_001.fastq
            #Step 1: read QC
            fastp --in1 ../../HD5_2_S38_R1_001.fastq.gz --in2 ../../HD5_2_S38_R2_001.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            # NOTE that the following steps are not run since the short-reads are not correct!
            # #Step 2: Polypolish
            # for cluster in cluster_001 cluster_005; do
            #   bwa index ${cluster}/7_final_consensus.fasta
            #   bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            #   bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            #   polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            # done
            # #Step 3: POLCA
            # for cluster in cluster_001 cluster_005; do
            #   cd ${cluster}
            #   polca.sh -a polypolish.fasta -r "../../../HD5_2_S38_R1_001.fastq.gz ../../../HD5_2_S38_R2_001.fastq.gz" -t 55 -m 120G
            #   cd ..
            # done
            # #Step 4: (optional) more rounds POLCA
            # for cluster in cluster_001; do
            #   cd ${cluster}
            #   polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../HD5_2_S38_R1_001.fastq.gz ../../../HD5_2_S38_R2_001.fastq.gz" -t 55 -m 120G
            #   cd ..
            # done
            # NOTE that the plasmids of HD5_2_K5 and HD5_2_K6 were copied from Unicycler!
            #-- HD5_2_K5 (4) --
            mean read depth: 87.1x
            25 bp have a depth of zero (99.9990% coverage)
            1,085 positions changed (0.0433% of total positions)
            estimated pre-polishing sequence accuracy: 99.9567% (Q33.63)
            #Step 1: read QC
            fastp --in1 ../../275_K5_Holger_S92_R1_001.fastq.gz --in2 ../../275_K5_Holger_S92_R2_001.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../275_K5_Holger_S92_R1_001.fastq.gz ../../../275_K5_Holger_S92_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 146
            #Insertion/Deletion Errors: 2
            #Assembly Size: 2504401
            #Consensus Quality: 99.9941
            #Substitution Errors: 41
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 99.9007
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 9191
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2767
            #Consensus Quality: 100
            #Step 4: (optional) more rounds POLCA
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../275_K5_Holger_S92_R1_001.fastq.gz ../../../275_K5_Holger_S92_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 41
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504401
            #Consensus Quality: 99.9984
            #Substitution Errors: 8
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 99.9806
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../275_K5_Holger_S92_R1_001.fastq.gz ../../../275_K5_Holger_S92_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 8
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504401
            #Consensus Quality: 99.9997
            #Substitution Errors: 4
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 99.9903
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../275_K5_Holger_S92_R1_001.fastq.gz ../../../275_K5_Holger_S92_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 8
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504401
            #Consensus Quality: 99.9997
            #Substitution Errors: 4
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 99.9903
            #-- HD5_2_K6 (4) --
            #mean read depth: 116.7x
            #4 bp have a depth of zero (99.9998% coverage)
            #1,022 positions changed (0.0408% of total positions)
            #estimated pre-polishing sequence accuracy: 99.9592% (Q33.89)
            #Step 1: read QC
            fastp --in1 ../../276_K6_Holger_S95_R1_001.fastq.gz --in2 ../../276_K6_Holger_S95_R2_001.fastq.gz --out1 1.fastq.gz --out2 2.fastq.gz --unpaired1 u.fastq.gz --unpaired2 u.fastq.gz
            #Step 2: Polypolish
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            bwa index ${cluster}/7_final_consensus.fasta
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 1.fastq.gz > ${cluster}/alignments_1.sam
            bwa mem -t 55 -a ${cluster}/7_final_consensus.fasta 2.fastq.gz > ${cluster}/alignments_2.sam
            polypolish polish ${cluster}/7_final_consensus.fasta ${cluster}/alignments_1.sam ${cluster}/alignments_2.sam > ${cluster}/polypolish.fasta
            done
            #Step 3: POLCA
            for cluster in cluster_001 cluster_002 cluster_003 cluster_004; do
            cd ${cluster}
            polca.sh -a polypolish.fasta -r "../../../276_K6_Holger_S95_R1_001.fastq.gz ../../../276_K6_Holger_S95_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 164
            #Insertion/Deletion Errors: 2
            #Assembly Size: 2504398
            #Consensus Quality: 99.9934
            #Substitution Errors: 22
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 99.9467
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 9191
            #Consensus Quality: 100
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2767
            #Consensus Quality: 100
            #Step 4: (optional) more rounds POLCA
            for cluster in cluster_001 cluster_002; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa -r "../../../276_K6_Holger_S95_R1_001.fastq.gz ../../../276_K6_Holger_S95_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 32
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504400
            #Consensus Quality: 99.9987
            #Substitution Errors: 0
            #Insertion/Deletion Errors: 0
            #Assembly Size: 41288
            #Consensus Quality: 100
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../276_K6_Holger_S95_R1_001.fastq.gz ../../../276_K6_Holger_S95_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 4
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504400
            #Consensus Quality: 99.9998
            for cluster in cluster_001; do
            cd ${cluster}
            polca.sh -a polypolish.fasta.PolcaCorrected.fa.PolcaCorrected.fa.PolcaCorrected.fa -r "../../../276_K6_Holger_S95_R1_001.fastq.gz ../../../276_K6_Holger_S95_R2_001.fastq.gz" -t 55 -m 120G
            cd ..
            done
            #Substitution Errors: 2
            #Insertion/Deletion Errors: 0
            #Assembly Size: 2504400
            #Consensus Quality: 99.9999

24. (Optional) use platon to confirm the plasmid contigs: https://github.com/oschwengers/platon

🔹 使用前

• 检查锯片是否完好、紧固。
• 确认护罩、分离刀、防反弹爪安装正确。
• 戴好护目镜、耳罩，避免宽松衣物。

🔹 操作中

• 双脚稳站，身体偏向一侧，不要正对锯片。
• 使用 靠山（Fence） 保持直线切割。
• 窄料必须用 推杆/推块(miter gauge)，不要手靠近锯片。
• 保持推料匀速，不要强行推进或扭动工件。

⚠️ 重点危险：反弹（Kickback）

• 定义：工件夹住锯片后，被高速抛向操作者。
• 常见原因：
  • 缺少分离刀或安装不当。
  • 工件没有紧贴靠山，自由手推料。
  • 木料弯曲、节疤、潮湿变形。
  • 锯片钝或不合适。
• 预防措施：
  • 保持分离刀、防反弹爪正常工作。
  • 工件必须紧贴靠山，直线推送。
  • 检查木料质量，保持锯片锋利。
  • 操作者站位偏侧，避免正对锯片。

🔹 使用后

• 关闭电源，等待锯片完全停止。
• 使用刷子或吸尘器清理锯屑，切勿用手。

✅ 牢记：分离刀 + 正确推料 + 正确站位 = 避免反弹

乌斯怀亚是阿根廷火地省（Tierra del Fuego）的首府，被誉为 “世界最南端的城市”。
它不仅是地理意义上的极南之地，也是通往南极洲的重要门户。

📍 地理位置

• 位于南美洲最南端，濒临 比格尔海峡 (Beagle Channel)。
• 北靠 马夏尔山脉 (Martial Mountains)，南临大海，地形独特。
• 纬度大约在 南纬 54°48′，几乎接近南极圈。

🌍 城市特色

• 世界最南端的城市：比智利的蓬塔阿雷纳斯更靠南，因此享有“世界尽头 (Fin del Mundo)”的称号。
• 气候属于 副极地海洋性气候：
  • 夏季（12–2月）气温约 6–15°C
  • 冬季（6–8月）气温常在 -2–5°C
• 一年四季多变，常常一天之内体验四季。

🏔️ 自然环境

• 雪山：马夏尔山脉常年白雪皑皑，适合滑雪与登山。
• 海洋：比格尔海峡水域中有丰富的海洋生物，包括企鹅、海狮、鲸鱼。
• 森林与苔原：周边有大片冷温带森林，是徒步和露营的理想地。

🚢 旅游与探险

• 南极游轮出发点：大多数前往南极洲的探险船与邮轮都从乌斯怀亚出发。
• 户外活动：滑雪、徒步、皮划艇、观鸟、野生动物摄影。
• 知名景点：
  • 火地国家公园 (Parque Nacional Tierra del Fuego)
  • 世界尽头博物馆 (Museo del Fin del Mundo)
  • 世界尽头火车 (Tren del Fin del Mundo)

👥 人口与社会

• 人口大约 75,000–80,000 人。
• 城市中既有当地的原住民后裔，也有来自欧洲与其他南美国家的移民。
• 以旅游业、渔业、科研和军队驻地为主要经济支柱。

🕰️ 历史背景

• 原住民：最早居住在此的是 雅干人 (Yaghan, 也称Yámana)，他们是世界上最南端的原住民族之一。
• 欧洲殖民：19世纪时，英国传教士与阿根廷殖民者进入该地。
• 刑罚殖民地：乌斯怀亚曾在20世纪初被阿根廷政府用作流放犯人的监狱城，这一点与澳大利亚的塔斯马尼亚有些相似。
• 之后逐渐发展为一个行政与军事中心，后来旅游业和科研活动成为主要推动力。

✨ 总结

乌斯怀亚不仅仅是 “世界尽头” 的代名词，
它是一个兼具 壮丽自然景观、探险精神与文化历史 的城市。
无论是作为前往南极的门户，还是南美南端的冒险目的地，乌斯怀亚都拥有独一无二的魅力。

author: ""
date: '`r format(Sys.time(), "%d %m %Y")`'
header-includes:
     - \usepackage{color, fancyvrb}
output:
    rmdformats::readthedown:
        highlight: kate
        number_sections : yes
    pdf_document:
        toc: yes
        toc_depth: 2
        number_sections : yes
---

```{r, echo=FALSE, warning=FALSE}
## Global options
# TODO: reproduce the html with the additional figure/SVN-files for editing.
# IMPORTANT NOTE: needs before "mkdir figures"
#NEEDs to be often close R and start R, then new rendering --> working!
#rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
#install.packages("heatmaply")
#install.packages("gplots")
#BiocManager::install("phyloseq")
#library(phyloseq)
#DEBUG a package conflict: using phyloseq::tax_table() or detach(package:MicrobiotaProcess, unload=TRUE)
```

```{r load-packages, include=FALSE}

#install.packages(c("picante", "rmdformats"))
#mamba install -c conda-forge freetype libpng harfbuzz fribidi
#mamba install -c conda-forge r-systemfonts r-svglite r-kableExtra freetype fontconfig harfbuzz fribidi libpng
library(knitr)
library(rmdformats)
library(readxl)
library(dplyr)
library(kableExtra)
library(openxlsx)
library(DESeq2)
library(writexl)

options(max.print="75")
knitr::opts_chunk$set(fig.width=8,
                                            fig.height=6,
                                            eval=TRUE,
                                            cache=TRUE,
                                            echo=TRUE,
                                            prompt=FALSE,
                                            tidy=FALSE,
                                            comment=NA,
                                            message=FALSE,
                                            warning=FALSE)
opts_knit$set(width=85)
# Phyloseq R library
#* Phyloseq web site : https://joey711.github.io/phyloseq/index.html
#* See in particular tutorials for
#    - importing data: https://joey711.github.io/phyloseq/import-data.html
#    - heat maps: https://joey711.github.io/phyloseq/plot_heatmap-examples.html
```

# Data

Import raw data and assign sample key:

```{r, echo=FALSE, warning=FALSE}
#extend qiime2_metadata_for_qza_to_phyloseq.tsv with Diet and Flora
#setwd("~/DATA/Data_Laura_16S_2/core_diversity_e4753")
#map_corrected <- read.csv("qiime2_metadata_for_qza_to_phyloseq.tsv", sep="\t", row.names=1)
#knitr::kable(map_corrected) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

# Prerequisites to be installed

* R : https://pbil.univ-lyon1.fr/CRAN/
* R studio : https://www.rstudio.com/products/rstudio/download/#download

```R
install.packages("dplyr")     # To manipulate dataframes
install.packages("readxl")    # To read Excel files into R
install.packages("ggplot2")   # for high quality graphics
install.packages("heatmaply")
source("https://bioconductor.org/biocLite.R")
biocLite("phyloseq")
```

```{r libraries, echo=TRUE, message=FALSE}
#mamba install -c conda-forge r-ggplot2 r-vegan r-data.table
#BiocManager::install("microbiome")
#install.packages("ggpubr")
#install.packages("heatmaply")
library("readxl") # necessary to import the data from Excel file
library("ggplot2") # graphics
library("picante")
library("microbiome") # data analysis and visualisation
library("phyloseq") # also the basis of data object. Data analysis and visualisation
library("ggpubr") # publication quality figures, based on ggplot2
library("dplyr") # data handling, filter and reformat data frames
library("RColorBrewer") # nice color options
library("heatmaply")
library(vegan)
library(gplots)
#install.packages("openxlsx")
library(openxlsx)
```

# Read the data and create phyloseq objects

Three tables are needed

* OTU
* Taxonomy
* Samples

```{r, echo=FALSE, warning=FALSE}

        library(tidyr)

        # For QIIME1
        #ps.ng.tax <- import_biom("./exported_table/feature-table.biom", "./exported-tree/tree.nwk")

        # For QIIME2
        #install.packages("remotes")
        #remotes::install_github("jbisanz/qiime2R")
        #"core_metrics_results/rarefied_table.qza", rarefying performed in the code, therefore import the raw table.
        library(qiime2R)
        ps.ng.tax <- qza_to_phyloseq(
            features =  "dada2_tests2/test_7_f240_r240/table.qza",
            tree = "rooted-tree.qza",
            metadata = "qiime2_metadata_for_qza_to_phyloseq.tsv"
        )
        # or
        #biom convert \
        #      -i ./exported_table/feature-table.biom \
        #      -o ./exported_table/feature-table-v1.biom \
        #      --to-json
        #ps.ng.tax <- import_biom("./exported_table/feature-table-v1.biom", treefilename="./exported-tree/tree.nwk")

        sample <- read.csv("./qiime2_metadata_for_qza_to_phyloseq.tsv", sep="\t", row.names=1)
        SAM = sample_data(sample, errorIfNULL = T)
        #rownames(SAM) <- c("1","2","3","5","6","7","8","9","10","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","46","47","48","49","50","51","52","53","55")

        #> setdiff(rownames(SAM), sample_names(ps.ng.tax))
        #[1] "sample-L9" should be removed since the low reads

        ps.ng.tax <- merge_phyloseq(ps.ng.tax, SAM)
        print(ps.ng.tax)

        taxonomy <- read.delim("exported-taxonomy/taxonomy.tsv", sep="\t", header=TRUE)
        #head(taxonomy)
        # Separate taxonomy string into separate ranks
        taxonomy_df <- taxonomy %>% separate(Taxon, into = c("Domain","Phylum","Class","Order","Family","Genus","Species"), sep = ";", fill = "right", extra = "drop")
        # Use Feature.ID as rownames
        rownames(taxonomy_df) <- taxonomy_df$Feature.ID
        taxonomy_df <- taxonomy_df[, -c(1, ncol(taxonomy_df))]  # Drop Feature.ID and Confidence
        # Create tax_table
        tax_table_final <- phyloseq::tax_table(as.matrix(taxonomy_df))
        # Merge tax_table with existing phyloseq object
        ps.ng.tax <- merge_phyloseq(ps.ng.tax, tax_table_final)
        # Check
        ps.ng.tax

        #colnames(phyloseq::tax_table(ps.ng.tax)) <- c("Domain","Phylum","Class","Order","Family","Genus","Species")
        saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
```

Visualize data
```{r, echo=TRUE, warning=FALSE}
    sample_names(ps.ng.tax)
    rank_names(ps.ng.tax)
    sample_variables(ps.ng.tax)

    # Define sample names once
    samples <- c(
        "sample-A1","sample-A2","sample-A3","sample-A8","sample-A9","sample-A10",  #RESIZED: "sample-A4","sample-A5","sample-A6","sample-A7","sample-A11",
        "sample-B10","sample-B11","sample-B12","sample-B13","sample-B14","sample-B15","sample-B16",  #RESIZED: "sample-B1","sample-B2","sample-B3","sample-B4","sample-B5","sample-B6","sample-B7","sample-B8","sample-B9",
        "sample-C3","sample-C4","sample-C5","sample-C6","sample-C7",  #RESIZED: "sample-C1","sample-C2","sample-C8","sample-C9","sample-C10",
        "sample-E4","sample-E5","sample-E6","sample-E7","sample-E8",  #RESIZED: "sample-E1","sample-E2","sample-E3","sample-E9","sample-E10",
        "sample-F1","sample-F2","sample-F3","sample-F4","sample-F5",
        "sample-G1","sample-G2","sample-G3","sample-G4","sample-G5","sample-G6",
        "sample-H1","sample-H2","sample-H3","sample-H4","sample-H5","sample-H6",
        "sample-I1","sample-I2","sample-I3","sample-I4","sample-I5","sample-I6",
        "sample-J1","sample-J2","sample-J3","sample-J4","sample-J10","sample-J11",  #RESIZED: "sample-J5","sample-J6","sample-J7","sample-J8","sample-J9",
        "sample-K7","sample-K8","sample-K9","sample-K10",  #RESIZED: "sample-K1","sample-K2","sample-K3","sample-K4","sample-K5","sample-K6",  "sample-K11","sample-K12","sample-K13","sample-K14","sample-K15",
        "sample-L1","sample-L7","sample-L8","sample-L10",  #RESIZED: "sample-L2","sample-L3","sample-L4","sample-L5","sample-L6",  "sample-L11","sample-L12","sample-L13","sample-L14","sample-L15",
        "sample-M1","sample-M2","sample-M3","sample-M4","sample-M5","sample-M6","sample-M7","sample-M8",
        "sample-N1","sample-N2","sample-N3","sample-N4","sample-N5","sample-N6","sample-N7","sample-N8","sample-N9","sample-N10",
        "sample-O1","sample-O2","sample-O3","sample-O4","sample-O5","sample-O6","sample-O7","sample-O8"
    )
    ps.ng.tax <- prune_samples(samples, ps.ng.tax)

    sample_names(ps.ng.tax)
    rank_names(ps.ng.tax)
    sample_variables(ps.ng.tax)
```

Normalize number of reads in each sample using median sequencing depth.
```{r, echo=TRUE, warning=FALSE}
# RAREFACTION
set.seed(9242)  # This will help in reproducing the filtering and nomalisation.
ps.ng.tax <- rarefy_even_depth(ps.ng.tax, sample.size = 6389)
total <- 6389

# NORMALIZE number of reads in each sample using median sequencing depth.
total = median(sample_sums(ps.ng.tax))
#> total
#[1] 42369
standf = function(x, t=total) round(t * (x / sum(x)))
ps.ng.tax = transform_sample_counts(ps.ng.tax, standf)
ps.ng.tax_rel <- microbiome::transform(ps.ng.tax, "compositional")

saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
hmp.meta <- meta(ps.ng.tax)
hmp.meta$sam_name <- rownames(hmp.meta)
```

# Prepare ps.ng.tax_rel, ps.ng.tax_abund, ps.ng.tax_abund_rel from ps.ng.tax

```{r, echo=FALSE, warning=FALSE}
#MOVE_FROM_ABOVE: The number of reads used for normalization is **`r sprintf("%.0f", total)`**.
#A basic heatmap using the default parameters.
#  plot_heatmap(ps.ng.tax, method = "NMDS", distance = "bray")
#NOTE that giving the correct OTU numbers in the text (1%, 0.5%, ...)!!!
```

For the heatmaps, we focus on the most abundant OTUs by first converting counts to relative abundances within each sample. We then filter to retain only OTUs whose mean relative abundance across all samples exceeds 0.1% (0.001). We are left with 199 OTUs which makes the reading much more easy.

```{r, echo=FALSE, warning=FALSE}

# Custom function to plot a heatmap with the specified sample order
#plot_heatmap_custom <- function(ps, sample_order, method = "NMDS", distance = "bray") {

# --Filtering strategy 1: This filters taxa based on raw counts (ps.ng.tax). For each taxon (across samples), it checks if it has a count that exceeds 1% of the total in at least one sample.     Description: We consider the most abundant OTUs for heatmaps. For example one can only take OTUs that represent at least 1% of reads in at least one sample. Remember we normalized all the sampples to median number of reads (total).  We are left with only 382 OTUS which makes the reading much more easy.
#ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)

# --Filtering strategy 2: This filters taxa based on relative abundances (ps.ng.tax_rel). It keeps only those taxa whose mean relative abundance across samples exceeds 0.1%.
# 1) Convert to relative abundances
ps.ng.tax_rel <- transform_sample_counts(ps.ng.tax, function(x) x / sum(x))

# 2) Get the logical vector of which OTUs to keep (based on relative abundance)
keep_vector <- phyloseq::filter_taxa(
    ps.ng.tax_rel,
    function(x) mean(x) > 0.001,
    prune = FALSE
)

# 3) Use the TRUE/FALSE vector to subset absolute abundance data
ps.ng.tax_abund <- prune_taxa(names(keep_vector)[keep_vector], ps.ng.tax)

# 4) Normalize the final subset to relative abundances per sample
ps.ng.tax_abund_rel <- transform_sample_counts(
    ps.ng.tax_abund,
    function(x) x / sum(x)
)
```

# Heatmaps

```{r, echo=FALSE, warning=FALSE}
datamat_ = as.data.frame(otu_table(ps.ng.tax_abund))

#datamat <- datamat_[c("1","2","5","6","7",  "8","9","10","12","13","14",    "15","16","17","18","19","20",  "21","22","23","24","25","26","27","28",    "29","30","31","32",  "33","34","35","36","37","38","39","51",    "40","41","42","43","44","46",  "47","48","49","50","52","53","55")]
datamat <- datamat_[c(
        "sample-A1","sample-A2","sample-A3","sample-A8","sample-A9","sample-A10",  #RESIZED: "sample-A4","sample-A5","sample-A6","sample-A7","sample-A11",
        "sample-B10","sample-B11","sample-B12","sample-B13","sample-B14","sample-B15","sample-B16",  #RESIZED: "sample-B1","sample-B2","sample-B3","sample-B4","sample-B5","sample-B6","sample-B7","sample-B8","sample-B9",
        "sample-C3","sample-C4","sample-C5","sample-C6","sample-C7",  #RESIZED: "sample-C1","sample-C2","sample-C8","sample-C9","sample-C10",
        "sample-E4","sample-E5","sample-E6","sample-E7","sample-E8",  #RESIZED: "sample-E1","sample-E2","sample-E3","sample-E9","sample-E10",
        "sample-F1","sample-F2","sample-F3","sample-F4","sample-F5",
        "sample-G1","sample-G2","sample-G3","sample-G4","sample-G5","sample-G6",
        "sample-H1","sample-H2","sample-H3","sample-H4","sample-H5","sample-H6",
        "sample-I1","sample-I2","sample-I3","sample-I4","sample-I5","sample-I6",
        "sample-J1","sample-J2","sample-J3","sample-J4","sample-J10","sample-J11",  #RESIZED: "sample-J5","sample-J6","sample-J7","sample-J8","sample-J9",
        "sample-K7","sample-K8","sample-K9","sample-K10",  #RESIZED: "sample-K1","sample-K2","sample-K3","sample-K4","sample-K5","sample-K6",  "sample-K11","sample-K12","sample-K13","sample-K14","sample-K15",
        "sample-L1","sample-L7","sample-L8","sample-L10",  #RESIZED: "sample-L2","sample-L3","sample-L4","sample-L5","sample-L6",  "sample-L11","sample-L12","sample-L13","sample-L14","sample-L15",
        "sample-M1","sample-M2","sample-M3","sample-M4","sample-M5","sample-M6","sample-M7","sample-M8",
        "sample-N1","sample-N2","sample-N3","sample-N4","sample-N5","sample-N6","sample-N7","sample-N8","sample-N9","sample-N10",
        "sample-O1","sample-O2","sample-O3","sample-O4","sample-O5","sample-O6","sample-O7","sample-O8"
    )]
# Remove rows with zero variance
datamat <- datamat[apply(datamat, 1, var) > 0, ]
# Remove cols with zero variance
#datamat <- datamat[, apply(datamat, 2, var) > 0]

hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
mycl = cutree(hr, h=max(hr$height)/1.08)
mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");

mycol = mycol[as.vector(mycl)]
sampleCols <- rep('GREY',ncol(datamat))
#names(sampleCols) <- c("Group1", "Group1", "Group1", "Group1", "Group1",   "Group2", "Group2",   "Group3", "Group3", "Group3",  ...)

# Define 14 colors
my_colors <- c("#a6cee3", "#1f78b4", "#b2df8a", "#33a02c",
                                "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00",
                                "#cab2d6", "#6a3d9a", "#ffff99", "#b15928",
                                "#8dd3c7", "#fb8072")
# Example column names
colnames(datamat) <- c(
        "sample-A1","sample-A2","sample-A3","sample-A8","sample-A9","sample-A10",  #RESIZED: "sample-A4","sample-A5","sample-A6","sample-A7","sample-A11",
        "sample-B10","sample-B11","sample-B12","sample-B13","sample-B14","sample-B15","sample-B16",  #RESIZED: "sample-B1","sample-B2","sample-B3","sample-B4","sample-B5","sample-B6","sample-B7","sample-B8","sample-B9",
        "sample-C3","sample-C4","sample-C5","sample-C6","sample-C7",  #RESIZED: "sample-C1","sample-C2","sample-C8","sample-C9","sample-C10",
        "sample-E4","sample-E5","sample-E6","sample-E7","sample-E8",  #RESIZED: "sample-E1","sample-E2","sample-E3","sample-E9","sample-E10",
        "sample-F1","sample-F2","sample-F3","sample-F4","sample-F5",
        "sample-G1","sample-G2","sample-G3","sample-G4","sample-G5","sample-G6",
        "sample-H1","sample-H2","sample-H3","sample-H4","sample-H5","sample-H6",
        "sample-I1","sample-I2","sample-I3","sample-I4","sample-I5","sample-I6",
        "sample-J1","sample-J2","sample-J3","sample-J4","sample-J10","sample-J11",  #RESIZED: "sample-J5","sample-J6","sample-J7","sample-J8","sample-J9",
        "sample-K7","sample-K8","sample-K9","sample-K10",  #RESIZED: "sample-K1","sample-K2","sample-K3","sample-K4","sample-K5","sample-K6",  "sample-K11","sample-K12","sample-K13","sample-K14","sample-K15",
        "sample-L1","sample-L7","sample-L8","sample-L10",  #RESIZED: "sample-L2","sample-L3","sample-L4","sample-L5","sample-L6",  "sample-L11","sample-L12","sample-L13","sample-L14","sample-L15",
        "sample-M1","sample-M2","sample-M3","sample-M4","sample-M5","sample-M6","sample-M7","sample-M8",
        "sample-N1","sample-N2","sample-N3","sample-N4","sample-N5","sample-N6","sample-N7","sample-N8","sample-N9","sample-N10",
        "sample-O1","sample-O2","sample-O3","sample-O4","sample-O5","sample-O6","sample-O7","sample-O8"
    )
# (replace with your actual column names)

# Remove "sample-" prefix for easier matching
sample_names <- sub("^sample-", "", colnames(datamat))

# Create a function to match sample IDs to groups
assign_group <- function(sample_id) {
    # First letter indicates group
    prefix <- substr(sample_id, 1, 1)
    switch(prefix,
                 "A" = 1,
                 "B" = 2,
                 "C" = 3,
                 "E" = 4,
                 "F" = 5,
                 "G" = 6,
                 "H" = 7,
                 "I" = 8,
                 "J" = 9,
                 "K" = 10,
                 "L" = 11,
                 "M" = 12,
                 "N" = 13,
                 "O" = 14,
                 NA)
}
# Assign group numbers to samples
group_numbers <- sapply(sample_names, assign_group)
# Assign colors based on group numbers
sampleCols <- my_colors[group_numbers]
# Check results
print(sampleCols)
#'#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99', '#e31a1c', '#cab2d6', '#6a3d9a'

#bluered(75)
#color_pattern <- colorRampPalette(c("blue", "white", "red"))(100)
library(RColorBrewer)
custom_palette <- colorRampPalette(brewer.pal(9, "Blues"))
heatmap_colors <- custom_palette(100)
#colors <- heatmap_color_default(100)
png("figures/heatmap.png", width=1200, height=2400)
#par(mar=c(2, 2, 2, 2))  , lwid=1    lhei=c(0.7, 10)) # Adjust height of color keys   keysize=0.3,
heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
                        scale='row',trace='none',col=heatmap_colors, cexRow=1.2, cexCol=1.5,
                        RowSideColors = mycol, ColSideColors = sampleCols, srtCol=15, labRow=row.names(datamat), key=TRUE, margins=c(10, 15), lhei=c(0.7, 15), lwid=c(1,8))
dev.off()
```

```{r, echo=TRUE, warning=FALSE, fig.cap="Heatmap", out.width = '100%', fig.align= "center"}
knitr::include_graphics("./figures/heatmap.png")
```

\pagebreak

```{r, echo=FALSE, warning=FALSE}
    library(stringr)
#FITTING1:
#for id in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100  101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199; do
#for id in 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300; do
#for id in 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382; do
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Domain\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Domain\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Phylum\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Phylum\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Class\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Class\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Order\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Order\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Family\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Family\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Genus\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Genus\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Species\"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[${id},\"Species\"], \"__\")[[1]][2]"
#done

phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[1,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[2,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[3,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[4,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[5,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[6,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[7,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[8,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[9,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[10,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[11,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[12,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[13,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[14,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[15,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[16,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[17,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[18,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[19,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[20,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[21,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[22,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[23,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[24,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[25,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[26,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[27,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[28,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[29,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[30,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[31,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[32,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[33,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[34,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[35,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[36,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[37,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[38,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[39,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[40,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[41,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[42,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[43,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[44,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[45,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[46,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[47,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[48,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[49,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[50,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[51,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[52,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[53,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[54,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[55,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[56,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[57,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[58,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[59,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[60,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[61,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[62,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[63,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[64,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[65,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[66,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[67,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[68,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[69,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[70,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[71,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[72,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[73,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[74,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[75,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[76,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[77,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[78,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[79,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[80,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[81,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[82,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[83,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[84,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[85,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[86,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[87,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[88,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[89,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[90,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[91,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[92,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[93,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[94,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[95,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[96,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[97,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[98,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[99,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[100,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[101,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[102,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[103,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[104,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[105,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[106,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[107,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[108,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[109,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[110,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[111,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[112,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[113,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[114,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[115,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[116,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[117,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[118,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[119,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[120,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[121,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[122,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[123,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[124,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[125,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[126,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[127,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[128,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[129,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[130,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[131,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[132,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[133,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[134,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[135,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[136,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[137,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[138,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[139,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[140,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[141,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[142,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[143,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[144,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[145,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[146,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[147,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[148,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[149,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[150,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[151,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[152,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[153,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[154,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[155,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[156,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[157,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[158,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[159,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[160,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[161,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[162,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[163,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[164,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[165,"Species"], "__")[[1]][2]

phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[166,"Species"], "__")[[1]][2]

phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Species"], "__")[[1]][2]

phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[167,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[168,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[169,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[170,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[171,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[172,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[173,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[174,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[175,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[176,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[177,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[178,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[179,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[180,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[181,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[182,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[183,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[184,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[185,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[186,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[187,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[188,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[189,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[190,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[191,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[192,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[193,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[194,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[195,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[196,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[197,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[198,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Domain"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Phylum"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Class"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Order"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Family"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Genus"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Species"] <- str_split(phyloseq::tax_table(ps.ng.tax_abund_rel)[199,"Species"], "__")[[1]][2]

```

# Taxonomic summary

## Bar plots in phylum level

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
    #aes(color="Phylum", fill="Phylum") --> aes()
    #ggplot(data=data, aes(x=Sample, y=Abundance, fill=Phylum))
    my_colors <- c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")
    plot_bar(ps.ng.tax_abund_rel, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))                                  #6 instead of theme.size
```
```{r, echo=FALSE, warning=FALSE}
    #png("abc.png")
    #knitr::include_graphics("./Phyloseq_files/figure-html/unnamed-chunk-7-1.png")
    #dev.off()
```

\pagebreak
Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.

```{r, echo=TRUE, warning=FALSE}
    ps.ng.tax_abund_rel_pre_post_stroke <- merge_samples(ps.ng.tax_abund_rel, "pre_post_stroke")
    #PENDING: The effect weighted twice by sum(x), is the same to the effect weighted once directly from absolute abundance?!
    ps.ng.tax_abund_rel_pre_post_stroke_ = transform_sample_counts(ps.ng.tax_abund_rel_pre_post_stroke, function(x) x / sum(x))
    #plot_bar(ps.ng.tax_abund_relSampleType_, fill = "Phylum") + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack")
    plot_bar(ps.ng.tax_abund_rel_pre_post_stroke_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"))
```

```{r, echo=FALSE, warning=FALSE}

    #FITTING6: regulate the bar height if it has replicates: 11+16+10+10+5+6+6+6+11+15+14+8+10+8=136

    ps.ng.tax_abund_rel_weighted <- data.table::copy(ps.ng.tax_abund_rel)

    # Group1
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A1")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A2")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A3")]/6
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A4")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A5")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A6")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A7")]/11
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A8")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A9")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A10")]/6
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-A11")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-A11")]/11

    # Group2
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B1")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B2")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B3")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B4")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B5")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B6")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B7")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B8")]/16
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B9")]/16
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B10")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B11")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B11")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B12")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B12")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B13")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B13")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B14")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B14")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B15")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B15")]/7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-B16")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-B16")]/7

    # Group3 # Choosing C3-C7 due to cage-filter
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C1")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C2")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C3")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C4")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C5")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C6")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C7")]/5
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C8")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C9")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-C10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-C10")]/10

    # Group4 # Choosing E4-E8 due to cage-filter
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E1")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E2")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E3")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E4")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E5")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E6")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E7")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E8")]/5
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E9")]/10
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-E10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-E10")]/10

    # Group5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-F1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-F1")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-F2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-F2")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-F3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-F3")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-F4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-F4")]/5
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-F5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-F5")]/5

    # Group6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G1")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G2")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G3")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G4")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G5")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-G6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-G6")]/6

    # Group7
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H1")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H2")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H3")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H4")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H5")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-H6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-H6")]/6

    # Group8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I1")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I2")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I3")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I4")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I5")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-I6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-I6")]/6

    # Group9 #RESIZED:
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J1")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J2")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J3")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J4")]/6
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J5")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J6")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J7")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J8")]/11
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J9")]/11
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J10")]/6
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-J11")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-J11")]/6

    # Group10 #RESIZED:
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K1")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K2")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K3")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K4")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K5")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K6")]/15
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K7")]/4
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K8")]/4
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K9")]/4
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K10")]/4
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K11")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K11")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K12")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K12")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K13")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K13")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K14")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K14")]/15
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-K15")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-K15")]/15

    # Group11 #RESIZED:
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L1")]/4
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L2")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L3")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L4")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L5")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L6")]/14
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L7")]/4
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L8")]/4
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L10")]/4
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L11")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L11")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L12")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L12")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L13")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L13")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L14")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L14")]/14
    #otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-L15")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-L15")]/14

    # Group12
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M1")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M2")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M3")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M4")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M5")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M6")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M7")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-M8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-M8")]/8

    # Group13
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N1")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N10")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N10")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N2")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N3")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N4")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N5")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N6")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N7")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N8")]/10
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-N9")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-N9")]/10

    # Group14
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O1")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O1")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O2")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O2")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O3")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O3")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O4")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O4")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O5")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O5")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O6")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O6")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O7")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O7")]/8
    otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O8")] <- otu_table(ps.ng.tax_abund_rel)[,c("sample-O8")]/8

    sum(otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O1")])
    #[1] 0.125
    sum(otu_table(ps.ng.tax_abund_rel)[,c("sample-O1")])
    #[1] 1
```

\pagebreak
Use color according to phylum. Do separate panels Stroke and Sex_age.

```{r, echo=FALSE, warning=FALSE}
    #plot_bar(ps.ng.tax_abund_relswab_, x="Phylum", fill = "Phylum", facet_grid = Patient~RoundDay) + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack") + theme(axis.text = element_text(size = theme.size, colour="black"))
    plot_bar(ps.ng.tax_abund_rel_weighted, x="Phylum", fill="Phylum", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))
```

## Bar plots in class level

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
    my_colors <- c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")
    plot_bar(ps.ng.tax_abund_rel, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
```

Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps.ng.tax_abund_rel_pre_post_stroke_, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"))
```
\pagebreak

Use color according to class. Do separate panels Stroke and Sex_age.
```{r, echo=TRUE, warning=FALSE}
    #NOTE: MANALLY RUNNING the CODE by COPYING the CODE under R-console for the 6 blocks, then show them with knitr::include_graphics
    sum(otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O1")])
    plot_bar(ps.ng.tax_abund_rel_weighted, x="Class", fill="Class", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 准备数据
                ps_summary <- ps_df %>%
                    # 1. 只保留这三个 condition
                    filter(pre_post_stroke %in% c("pre.antibiotics", "baseline", "pre.stroke")) %>%

                    # 2. 聚合
                    group_by(Sex_age, pre_post_stroke, Class) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%

                    # 3. 设置 factor 顺序和重命名
                    mutate(
                        # 替换 Sex_age 名称
                        Sex_age = recode(Sex_age,
                                                        "female.aged" = "Female (Aged)",
                                                        "male.aged"   = "Male (Aged)",
                                                        "male.young"  = "Male (Young)"),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),

                        # 替换 condition 名称
                        pre_post_stroke = recode(pre_post_stroke,
                                                                        "pre.antibiotics" = "Pre Antibiotics",
                                                                        "baseline"        = "Baseline",
                                                                        "pre.stroke"      = "Pre Stroke"),
                        pre_post_stroke = factor(pre_post_stroke,
                                                                        levels = c("Pre Antibiotics", "Baseline", "Pre Stroke")),

                        Class = factor(Class)
                    )

                # 确保颜色数匹配
                class_levels <- levels(ps_summary$Class)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Class)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +  # 更窄的柱子
                    facet_grid(pre_post_stroke ~ ., scales = "free_x", drop = TRUE) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        strip.text = element_text(size = 10, face = "bold"),
                        legend.position = "right",               # ✅ legend 放右边
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    guides(fill = guide_legend(ncol = 1)) +     # 竖排图例
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Taxonomic Class Composition by Group and Condition"
                    )

                # 保存为 PNG 文件
                ggsave(
                    filename = "./figures/Separate_Stroke_and_SexAge_on_Class.png",
                    plot = p,
                    width = 8,
                    height = 6,
                    dpi = 200
                )

                knitr::include_graphics("./figures/Separate_Stroke_and_SexAge_on_Class.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.antibiotics") %>%
                    group_by(Sex_age, Class) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Class = factor(Class)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Class)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Class)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Class Composition - Pre Antibiotics"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_6-8_Pre_Antibiotics_Class_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_6-8_Pre_Antibiotics_Class_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}

# TODO _ERROR_NEXT_WEEK!!!!: why Group5 in baseline plots is extra low!, The plot is absolute number, it is not relative Abundance!!!!!
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "baseline") %>%
                    group_by(Sex_age, Class) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Class = factor(Class)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Class)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Class)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Class Composition - Baseline FMT donor"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_3-5_Baseline_FMT_donor_Class_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_3-5_Baseline_FMT_donor_Class_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 数据处理，只保留 "Pre Stroke"
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.stroke") %>%
                    group_by(Sex_age, Class) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Class = factor(Class)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Class)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Class)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Class Composition - Pre Stroke"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_9-11_Pre_Stroke_Class_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_9-11_Pre_Stroke_Class_Composition.png")
```

# Export Relative abundances of Phylum, Class, Order, and Family levels across all samples.

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
library(phyloseq)
library(writexl)
library(dplyr)

# Function to check for NA or empty values in a taxonomic rank
check_taxa_names <- function(tax_table, rank) {
    tax_values <- tax_table[[rank]]
    na_count <- sum(is.na(tax_values) | tax_values == "")
    cat("Number of NA or empty values in", rank, ":", na_count, "\n")
    if (na_count > 0) {
        cat("Taxa with NA or empty", rank, ":\n")
        print(tax_values[is.na(tax_values) | tax_values == ""])
    }
}

# Function to create and save relative abundance table for a given taxonomic rank with normalization
save_taxa_abundance <- function(ps, rank, output_file) {
    # Check for NA or empty values in the taxonomy table
    tax_table_df <- as.data.frame(tax_table(ps))
    check_taxa_names(tax_table_df, rank)

    # Aggregate OTUs by taxonomic rank, removing taxa with NA at the specified rank
    ps_glom <- tax_glom(ps, taxrank = rank, NArm = TRUE)

    # Extract OTU table (relative abundances)
    otu_table <- as.data.frame(otu_table(ps_glom))

    # Normalize each column to sum to 1
    otu_table_normalized <- apply(otu_table, 2, function(x) x / sum(x))

    # Convert matrix to data frame
    otu_table_normalized <- as.data.frame(otu_table_normalized)

    # Verify column sums are approximately 1.0
    col_sums <- colSums(otu_table_normalized)
    if (any(abs(col_sums - 1) > 1e-6)) {
        warning("Column sums in ", rank, " table do not equal 1.0: ", paste(col_sums, collapse = ", "))
    } else {
        cat("Column sums for ", rank, " table are all approximately 1.0\n")
    }

    # Extract taxonomy table and get the specified rank for taxa names
    tax_table_glom <- as.data.frame(tax_table(ps_glom))
    taxa_names <- tax_table_glom[[rank]]

    # Replace NA or empty strings with "Unclassified"
    taxa_names <- ifelse(is.na(taxa_names) | taxa_names == "", paste0("Unclassified_", rank), taxa_names)

    # Ensure unique row names
    taxa_names <- make.unique(taxa_names)

    # Set row names to taxa names (for internal reference)
    rownames(otu_table_normalized) <- taxa_names

    # Add taxa names as a column
    otu_table_normalized[[rank]] <- taxa_names

    # Reorder to move rank column to the first position
    otu_table_normalized <- otu_table_normalized[, c(rank, setdiff(names(otu_table_normalized), rank))]

    # Rename sample columns by removing "sample-" prefix
    names(otu_table_normalized)[-1] <- sub("sample-", "", names(otu_table_normalized)[-1])

    # Write the data frame to Excel, including the rank column
    write_xlsx(otu_table_normalized, path = output_file)
    cat("Saved", output_file, "\n")
}

# Verify column sums of ps.ng.tax_abund_rel
col_sums <- colSums(otu_table(ps.ng.tax_abund_rel))
cat("Column sums of ps.ng.tax_abund_rel:\n")
summary(col_sums)

# Generate Excel files for Phylum, Class, Order, and Family levels with normalization and renamed sample names
save_taxa_abundance(ps.ng.tax_abund_rel, "Phylum", "relative_abundance_phylum_old.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Class", "relative_abundance_class_old.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Order", "relative_abundance_order_old.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Family", "relative_abundance_family_old.xlsx")
```

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
library(phyloseq)
library(writexl)
library(dplyr)

# Function to check for NA or empty values in a taxonomic rank
check_taxa_names <- function(tax_table, rank) {
    tax_values <- tax_table[[rank]]
    na_count <- sum(is.na(tax_values) | tax_values == "")
    cat("Number of NA or empty values in", rank, ":", na_count, "\n")
    if (na_count > 0) {
        cat("Taxa with NA or empty", rank, ":\n")
        print(tax_values[is.na(tax_values) | tax_values == ""])
    }
}

# Function to create and save relative abundance table for a given taxonomic rank with normalization
save_taxa_abundance <- function(ps, rank, output_file) {
    # Clean the taxonomy table by removing D_[level]__ prefixes
    tax_table_df <- as.data.frame(tax_table(ps))
    tax_table_df[[rank]] <- ifelse(is.na(tax_table_df[[rank]]) | tax_table_df[[rank]] == "",
                                                                 paste0("Unclassified_", rank),
                                                                 sub("^D_[0-9]+__(.+)", "\\1", tax_table_df[[rank]]))
    tax_table(ps) <- as.matrix(tax_table_df)  # Update taxonomy table with cleaned names

    # Check for NA or empty values in the taxonomy table
    check_taxa_names(tax_table_df, rank)

    # Aggregate OTUs by taxonomic rank, removing taxa with NA at the specified rank
    ps_glom <- tax_glom(ps, taxrank = rank, NArm = TRUE)

    # Extract OTU table (relative abundances)
    otu_table <- as.data.frame(otu_table(ps_glom))

    # Normalize each column to sum to 1
    otu_table_normalized <- apply(otu_table, 2, function(x) x / sum(x))

    # Convert matrix to data frame
    otu_table_normalized <- as.data.frame(otu_table_normalized)

    # Verify column sums are approximately 1.0
    col_sums <- colSums(otu_table_normalized)
    if (any(abs(col_sums - 1) > 1e-6)) {
        warning("Column sums in ", rank, " table do not equal 1.0: ", paste(col_sums, collapse = ", "))
    } else {
        cat("Column sums for ", rank, " table are all approximately 1.0\n")
    }

    # Extract taxonomy table and get the specified rank for taxa names
    tax_table_glom <- as.data.frame(tax_table(ps_glom))
    taxa_names <- tax_table_glom[[rank]]

    # Ensure unique row names
    taxa_names <- make.unique(taxa_names)

    # Set row names to taxa names (for internal reference)
    rownames(otu_table_normalized) <- taxa_names

    # Add taxa names as a column
    otu_table_normalized[[rank]] <- taxa_names

    # Reorder to move rank column to the first position
    otu_table_normalized <- otu_table_normalized[, c(rank, setdiff(names(otu_table_normalized), rank))]

    # Rename sample columns by removing "sample-" prefix
    names(otu_table_normalized)[-1] <- sub("sample-", "", names(otu_table_normalized)[-1])

    # Write the data frame to Excel, including the rank column
    write_xlsx(otu_table_normalized, path = output_file)
    cat("Saved", output_file, "\n")
}

# Verify column sums of ps.ng.tax_abund_rel
col_sums <- colSums(otu_table(ps.ng.tax_abund_rel))
cat("Column sums of ps.ng.tax_abund_rel:\n")
summary(col_sums)

# Generate Excel files for Phylum, Class, Order, and Family levels with normalization and renamed sample names
save_taxa_abundance(ps.ng.tax_abund_rel, "Phylum", "relative_abundance_phylum.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Class", "relative_abundance_class.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Order", "relative_abundance_order.xlsx")
save_taxa_abundance(ps.ng.tax_abund_rel, "Family", "relative_abundance_family.xlsx")

#Sum up the last two colums with the same row.names to a new column, export the file as csv, then delete the two rows before last, then merge them with csv2xls to a Excel-file, adapt the sheet-names.

#~/Tools/csv2xls-0.4/csv_to_xls.py relative_abundance_phylum.csv relative_abundance_order.csv relative_abundance_family.csv -d$'\t' -o relative_abundance_phylum_order_family.xls;
```

## Bar plots in order level

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
    my_colors <- c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")
    plot_bar(ps.ng.tax_abund_rel, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
```

Regroup together pre vs post stroke and normalize number of reads in each group using median sequencing depth.
```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps.ng.tax_abund_rel_pre_post_stroke_, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"))
```
\pagebreak

Use color according to order. Do separate panels Stroke and Sex_age.
```{r, echo=FALSE, warning=FALSE}

    #FITTING7: regulate the bar height if it has replicates
    sum(otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O1")])
    plot_bar(ps.ng.tax_abund_rel_weighted, x="Order", fill="Order", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 准备数据
                ps_summary <- ps_df %>%
                    # 1. 只保留这三个 condition
                    filter(pre_post_stroke %in% c("pre.antibiotics", "baseline", "pre.stroke")) %>%

                    # 2. 聚合
                    group_by(Sex_age, pre_post_stroke, Order) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%

                    # 3. 设置 factor 顺序和重命名
                    mutate(
                        # 替换 Sex_age 名称
                        Sex_age = recode(Sex_age,
                                                        "female.aged" = "Female (Aged)",
                                                        "male.aged"   = "Male (Aged)",
                                                        "male.young"  = "Male (Young)"),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),

                        # 替换 condition 名称
                        pre_post_stroke = recode(pre_post_stroke,
                                                                        "pre.antibiotics" = "Pre Antibiotics",
                                                                        "baseline"        = "Baseline",
                                                                        "pre.stroke"      = "Pre Stroke"),
                        pre_post_stroke = factor(pre_post_stroke,
                                                                        levels = c("Pre Antibiotics", "Baseline", "Pre Stroke")),

                        Order = factor(Order)
                    )

                # 确保颜色数匹配
                class_levels <- levels(ps_summary$Order)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Order)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +  # 更窄的柱子
                    facet_grid(pre_post_stroke ~ ., scales = "free_x", drop = TRUE) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        strip.text = element_text(size = 10, face = "bold"),
                        legend.position = "right",               # ✅ legend 放右边
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    guides(fill = guide_legend(ncol = 1)) +     # 竖排图例
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Taxonomic Order Composition by Group and Condition"
                    )

                # 保存为 PNG 文件
                ggsave(
                    filename = "./figures/Separate_Stroke_and_SexAge_on_Order.png",
                    plot = p,
                    width = 8,
                    height = 6,
                    dpi = 200
                )

                knitr::include_graphics("./figures/Separate_Stroke_and_SexAge_on_Order.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.antibiotics") %>%
                    group_by(Sex_age, Order) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Order = factor(Order)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Order)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Order)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Order Composition - Pre Antibiotics"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_6-8_Pre_Antibiotics_Order_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_6-8_Pre_Antibiotics_Order_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "baseline") %>%
                    group_by(Sex_age, Order) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Order = factor(Order)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Order)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Order)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Order Composition - Baseline FMT donor"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_3-5_Baseline_FMT_donor_Order_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_3-5_Baseline_FMT_donor_Order_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.stroke") %>%
                    group_by(Sex_age, Order) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Order = factor(Order)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Order)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Order)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Order Composition - Pre Stroke"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_9-11_Pre_Stroke_Order_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_9-11_Pre_Stroke_Order_Composition.png")
```

## Bar plots in family level

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
    my_colors <- c(
                    "#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7"
                )
    plot_bar(ps.ng.tax_abund_rel, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
```

Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps.ng.tax_abund_rel_pre_post_stroke_, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"))
```
\pagebreak

Use color according to family. Do separate panels Stroke and Sex_age.
```{r, echo=TRUE, warning=FALSE}
    sum(otu_table(ps.ng.tax_abund_rel_weighted)[,c("sample-O1")])
    plot_bar(ps.ng.tax_abund_rel_weighted, x="Family", fill="Family", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
    scale_fill_manual(values = my_colors) + theme(axis.text = element_text(size = 7, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 准备数据
                ps_summary <- ps_df %>%
                    # 1. 只保留这三个 condition
                    filter(pre_post_stroke %in% c("pre.antibiotics", "baseline", "pre.stroke")) %>%

                    # 2. 聚合
                    group_by(Sex_age, pre_post_stroke, Family) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%

                    # 3. 设置 factor 顺序和重命名
                    mutate(
                        # 替换 Sex_age 名称
                        Sex_age = recode(Sex_age,
                                                        "female.aged" = "Female (Aged)",
                                                        "male.aged"   = "Male (Aged)",
                                                        "male.young"  = "Male (Young)"),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),

                        # 替换 condition 名称
                        pre_post_stroke = recode(pre_post_stroke,
                                                                        "pre.antibiotics" = "Pre Antibiotics",
                                                                        "baseline"        = "Baseline",
                                                                        "pre.stroke"      = "Pre Stroke"),
                        pre_post_stroke = factor(pre_post_stroke,
                                                                        levels = c("Pre Antibiotics", "Baseline", "Pre Stroke")),

                        Family = factor(Family)
                    )

                # 确保颜色数匹配
                class_levels <- levels(ps_summary$Family)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Family)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +  # 更窄的柱子
                    facet_grid(pre_post_stroke ~ ., scales = "free_x", drop = TRUE) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        strip.text = element_text(size = 10, face = "bold"),
                        legend.position = "right",               # ✅ legend 放右边
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    guides(fill = guide_legend(ncol = 2)) +     # 竖排图例
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Taxonomic Family Composition by Group and Condition"
                    )

                # 保存为 PNG 文件
                ggsave(
                    filename = "./figures/Separate_Stroke_and_SexAge_on_Family.png",
                    plot = p,
                    width = 9,
                    height = 6,
                    dpi = 200
                )

                knitr::include_graphics("./figures/Separate_Stroke_and_SexAge_on_Family.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 数据处理，只保留 "BL FMT donor"
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.antibiotics") %>%
                    group_by(Sex_age, Family) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Family = factor(Family)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Family)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Family)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Family Composition - Pre Antibiotics"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_6-8_Pre_Antibiotics_Family_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_6-8_Pre_Antibiotics_Family_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 数据处理，只保留 "BL FMT donor"
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "baseline") %>%
                    group_by(Sex_age, Family) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Family = factor(Family)
                    )

                # 映射颜色
                class_levels <- levels(ps_summary$Family)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Family)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Family Composition - Baseline FMT donor"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_3-5_Baseline_FMT_donor_Family_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_3-5_Baseline_FMT_donor_Family_Composition.png")
```

```{r, echo=FALSE, warning=FALSE}
                ps_df <- phyloseq::psmelt(ps.ng.tax_abund_rel_weighted)
                # 数据处理，只保留 "Pre Stroke"
                ps_summary <- ps_df %>%
                    filter(pre_post_stroke == "pre.stroke") %>%
                    group_by(Sex_age, Family) %>%
                    summarise(Abundance = sum(Abundance), .groups = "drop") %>%
                    mutate(
                        Sex_age = recode(Sex_age,
                            "female.aged" = "Female (Aged)",
                            "male.aged" = "Male (Aged)",
                            "male.young" = "Male (Young)"
                        ),
                        Sex_age = factor(Sex_age, levels = c("Male (Aged)", "Female (Aged)", "Male (Young)")),
                        Family = factor(Family)
                    )
                # Save summarized raw data
                write.csv(ps_summary, "Pre_Stroke_Family_Composition_data.csv", row.names = FALSE)
                write_xlsx(ps_summary, "Pre_Stroke_Family_Composition_data.xlsx")

                # 映射颜色
                class_levels <- levels(ps_summary$Family)
                color_map <- setNames(my_colors[seq_along(class_levels)], class_levels)

                # 绘图
                p <- ggplot(ps_summary, aes(x = Sex_age, y = Abundance, fill = Family)) +
                    geom_bar(stat = "identity", position = "stack", width = 0.55) +
                    scale_fill_manual(values = color_map, drop = FALSE) +
                    theme_minimal(base_size = 11) +
                    theme(
                        axis.text.x = element_text(angle = 45, hjust = 1, size = 9, colour = "black"),
                        axis.title = element_text(size = 11),
                        legend.position = "right",
                        legend.title = element_blank(),
                        panel.grid.major.x = element_blank(),
                        panel.grid.minor = element_blank()
                    ) +
                    labs(
                        x = "Sex and Age Group",
                        y = "Relative Abundance",
                        title = "Family Composition - Pre Stroke"
                    ) +
                    guides(fill = guide_legend(ncol = 2))

                # 保存图像
                ggsave(
                    filename = "./figures/Group_9-11_Pre_Stroke_Family_Composition.png",
                    plot = p,
                    width = 8,
                    height = 5,
                    dpi = 200
                )

                # 插入图像到报告
                knitr::include_graphics("./figures/Group_9-11_Pre_Stroke_Family_Composition.png")
```

\pagebreak

# Alpha diversity
Plot Chao1 richness estimator, Observed OTUs, Shannon index, and Phylogenetic diversity.
Regroup together samples from the same group.
```{r, echo=FALSE, warning=FALSE}
# using rarefied data
#FITTING2: CONSOLE:
#gunzip table_even4753.biom.gz
#alpha_diversity.py -i table_even42369.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering/rep_set.tre
#gunzip table_even4753.biom.gz
#alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_stool/rep_set.tre
#gunzip table_even4753.biom.gz
#alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_swab/rep_set.tre
```

```{r, echo=TRUE, warning=FALSE}
#fig.width=9, fig.height=6,
#QIIME1 hmp.div_qiime <- read.csv("adiv_even.txt", sep="\t")
#QIIME1 colnames(hmp.div_qiime) <- c("sam_name", "chao1", "observed_otus", "shannon", "PD_whole_tree")
#QIIME1 row.names(hmp.div_qiime) <- hmp.div_qiime$sam_name
#QIIME1 div.df <- merge(hmp.div_qiime, hmp.meta, by = "sam_name")
#QIIME1 div.df2 <- div.df[, c("Group", "chao1", "shannon", "observed_otus", "PD_whole_tree")]
#QIIME1 colnames(div.df2) <- c("Group", "Chao-1", "Shannon", "OTU", "Phylogenetic Diversity")
#QIIME1 options(max.print=999999)
#QIIME1 #27     H47 830.5000 5.008482 319               10.60177
#QIIME1 #FITTING4: if occuring "Computation failed in `stat_signif()`:not enough 'y' observations"
#QIIME1 #means: the patient H47 contains only one sample, it should be removed for the statistical p-values calculations.
#QIIME1 #delete H47(1)
#QIIME1 #div.df2 <- div.df2[-c(3), ]
#QIIME1 #div.df2 <- div.df2[-c(55,54, 45,40,39,27,26,25,1), ]

# for QIIME2: Lesen der Metriken
shannon <- read.table("exported_alpha/shannon/alpha-diversity.tsv", header=TRUE, sep="\t")
faith_pd <- read.table("exported_alpha/faith_pd/alpha-diversity.tsv", header=TRUE, sep="\t")
observed <- read.table("exported_alpha/observed_features/alpha-diversity.tsv", header=TRUE, sep="\t")
#chao1 <- read.table("exported_alpha/chao1/alpha-diversity.tsv", header=TRUE, sep="\t")    #TODO: Check the correctness of chao1-calculation.

# Umbenennen für Klarheit
colnames(shannon) <- c("sam_name", "shannon")
colnames(faith_pd) <- c("sam_name", "PD_whole_tree")
colnames(observed) <- c("sam_name", "observed_otus")
#colnames(chao1) <- c("sam_name", "chao1")

# Merge alles in ein DataFrame
div.df <- Reduce(function(x, y) merge(x, y, by="sam_name"),
                                    list(shannon, faith_pd, observed))

# Meta-Daten einfügen
div.df <- merge(div.df, hmp.meta, by="sam_name")

# Reformat
div.df2 <- div.df[, c("sam_name", "Group", "shannon", "observed_otus", "PD_whole_tree")]
colnames(div.df2) <- c("Sample name", "Group", "Shannon", "OTU", "Phylogenetic Diversity")
write.csv(div.df2, file="alpha_diversities.txt")
knitr::kable(div.df2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

#https://uc-r.github.io/t_test
#We can perform the test with t.test and transform our data and we can also perform the nonparametric test with the wilcox.test function.
stat.test.Shannon <- compare_means(
 Shannon ~ Group, data = div.df2,
 method = "t.test"
)
knitr::kable(stat.test.Shannon) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

div_df_melt <- reshape2::melt(div.df2)
#head(div_df_melt)

#https://plot.ly/r/box-plots/#horizontal-boxplot
#http://www.sthda.com/english/wiki/print.php?id=177
#https://rpkgs.datanovia.com/ggpubr/reference/as_ggplot.html
#http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/82-ggplot2-easy-way-to-change-graphical-parameters/
#https://plot.ly/r/box-plots/#horizontal-boxplot
#library("gridExtra")
#par(mfrow=c(4,1))
p <- ggboxplot(div_df_melt, x = "Group", y = "value",
                            facet.by = "variable",
                            scales = "free",
                            width = 0.5,
                            fill = "gray", legend= "right")
#ggpar(p, xlab = FALSE, ylab = FALSE)
lev <- levels(factor(div_df_melt$Group)) # get the variables
#FITTING4: delete H47(1) in lev
#lev <- lev[-c(3)]
# make a pairwise list that we want to compare.
#my_stat_compare_means
#https://stackoverflow.com/questions/47839988/indicating-significance-with-ggplot2-in-a-boxplot-with-multiple-groups
L.pairs <- combn(seq_along(lev), 2, simplify = FALSE, FUN = function(i) lev[i]) #%>% filter(p.signif != "ns")
my_stat_compare_means  <- function (mapping = NULL, data = NULL, method = NULL, paired = FALSE,
        method.args = list(), ref.group = NULL, comparisons = NULL,
        hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left",
        label.y.npc = "top", label.x = NULL, label.y = NULL, tip.length = 0.03,
        symnum.args = list(), geom = "text", position = "identity",
        na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
{
        if (!is.null(comparisons)) {
                method.info <- ggpubr:::.method_info(method)
                method <- method.info$method
                method.args <- ggpubr:::.add_item(method.args, paired = paired)
                if (method == "wilcox.test")
                        method.args$exact <- FALSE
                pms <- list(...)
                size <- ifelse(is.null(pms$size), 0.3, pms$size)
                color <- ifelse(is.null(pms$color), "black", pms$color)
                map_signif_level <- FALSE
                if (is.null(label))
                        label <- "p.format"
                if (ggpubr:::.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) {
                        if (ggpubr:::.is_empty(symnum.args)) {
                                map_signif_level <- c(`****` = 1e-04, `***` = 0.001,
                                    `**` = 0.01, `*` = 0.05, ns = 1)
                        } else {
                             map_signif_level <- symnum.args
                        }
                        if (hide.ns)
                                names(map_signif_level)[5] <- " "
                }
                step_increase <- ifelse(is.null(label.y), 0.12, 0)
                ggsignif::geom_signif(comparisons = comparisons, y_position = label.y,
                        test = method, test.args = method.args, step_increase = step_increase,
                        size = size, color = color, map_signif_level = map_signif_level,
                        tip_length = tip.length, data = data)
        } else {
                mapping <- ggpubr:::.update_mapping(mapping, label)
                layer(stat = StatCompareMeans, data = data, mapping = mapping,
                        geom = geom, position = position, show.legend = show.legend,
                        inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc,
                                label.y.npc = label.y.npc, label.x = label.x,
                                label.y = label.y, label.sep = label.sep, method = method,
                                method.args = method.args, paired = paired, ref.group = ref.group,
                                symnum.args = symnum.args, hide.ns = hide.ns,
                                na.rm = na.rm, ...))
        }
}

# Rotate the x-axis labels to 45 degrees and adjust their position
p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust=1, size=8))
#comparisons = list(c("Group1", "Group2"), c("Group3", "Group4")),
p2 <- p +
stat_compare_means(
    method="t.test",
    comparisons = list(),
    label = "p.signif",
    symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))
)
#comparisons = L.pairs,
#symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
#stat_pvalue_manual
print(p2)
#https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
#FITTING3: mkdir figures
ggsave("./figures/alpha_diversity_Group.png", device="png", height = 10, width = 15)
ggsave("./figures/alpha_diversity_Group.svg", device="svg", height = 10, width = 15)

#NOTE: Run this Phyloseq.Rmd, then run the code of MicrobiotaProcess.R to manually generate PCoA.png, then run this Phyloseq.Rmd!
#NOTE: AT_FIRST_DEACTIVATE_THIS_LINE: knitr::include_graphics("./figures/PCoA.png")

```

```{r, echo=FALSE, warning=FALSE, fig.cap="Alpha diversity", out.width = '100%', fig.align= "center"}
## MANUALLY selected alpha diversities unter host-env after 'cp alpha_diversities.txt selected_alpha_diversities.txt'
#knitr::include_graphics("./figures/alpha_diversity_Group.png")
#selected_alpha_diversities<-read.csv("selected_alpha_diversities.txt",sep="\t")
#knitr::kable(selected_alpha_diversities) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

# Beta diversity (Bray-Curtis distance)

## Group1 vs Group2
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#fig.cap="Beta diversity",

#for QIIME1: file:///home/jhuang/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/weighted_unifrac_boxplots/Group_Stats.txt

# -- for QIIME2: MANUALLY filter permanova-pairwise.csv and save as permanova-pairwise_.csv
# #grep "Permutations" exported_beta_group/permanova-pairwise.csv > permanova-pairwise_.csv
# #grep "Group1,Group2" exported_beta_group/permanova-pairwise.csv >> permanova-pairwise_.csv
# #grep "Group3,Group4" exported_beta_group/permanova-pairwise.csv >> permanova-pairwise_.csv
# beta_diversity_group_stats<-read.csv("permanova-pairwise_.csv",sep=",")
# #beta_diversity_group_stats <- beta_diversity_group_stats[beta_diversity_group_stats$Group.1 == "Group1" & beta_diversity_group_stats$Group.2 == "Group2", ]
# #beta_diversity_group_stats <- beta_diversity_group_stats[beta_diversity_group_stats$Group.1 == "Group3" & beta_diversity_group_stats$Group.2 == "Group4", ]
# knitr::kable(beta_diversity_group_stats) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

#NOTE: Run this Phyloseq.Rmd, then run the code of MicrobiotaProcess.R to manually generate Comparison_of_Bray_Distances_Group1_vs_Group2.png and Comparison_of_Bray_Distances_Group3_vs_Group4.png, then run this Phyloseq.Rmd!

#knitr::include_graphics("./figures/Comparison_of_Bray_Distances_Group1_vs_Group2.png")

```

## Group3 vs Group4
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/Comparison_of_Bray_Distances_Group3_vs_Group4.png")
```

# The PCoA analysis

## Group1 vs Group2
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/PCoA2_Group1_vs_Group2.png")
```

## Group3 vs Group4
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/PCoA2_Group3_vs_Group4.png")
```

## Groups 1, 2, 3 and 4
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/PCoA2_Group1-Group4.png")
```

## Groups 9,10, 11, 12,13, and 14
```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/PCoA2_Group9-Group14.png")
```

# Differential abundance analysis

Differential abundance analysis aims to find the differences in the abundance of each taxa between two groups of samples, assigning a significance value to each comparison.

## Group1 vs Group2

```{r, echo=TRUE, warning=FALSE}
#ps.ng.tax [ 2633 taxa and 136 samples] and ps.ng.tax_abund (absolute abundance)  [382 taxa and 136 samples],  ps.ng.tax_abund_rel (relative abundance)  [382 taxa and 136 samples], either ps.ng.tax and ps.ng.tax_abund can be used here!
ps.ng.tax_abund_sel1 <- data.table::copy(ps.ng.tax_abund)
otu_table(ps.ng.tax_abund_sel1) <- otu_table(ps.ng.tax_abund)[,c("sample-A1","sample-A2","sample-A3","sample-A8","sample-A9","sample-A10",   "sample-B10","sample-B11","sample-B12","sample-B13","sample-B14","sample-B15","sample-B16")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_abund_sel1, ~Group)
diagdds$Group <- relevel(diagdds$Group, "Group2")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)

res = results(diagdds, cooksCutoff = FALSE)
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(phyloseq::tax_table(ps.ng.tax_abund_sel1)[rownames(sigtab), ], "matrix"))
#sigtab <- sigtab[rownames(sigtab) %in% rownames(phyloseq::tax_table(ps.ng.tax_abund)), ]
kable(sigtab) %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
        scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
```

```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#knitr::include_graphics("./figures/diff_analysis_Group1_vs_Group2.png")
```

## Group3 vs Group4

```{r, echo=TRUE, warning=FALSE}
ps.ng.tax_abund_sel2 <- data.table::copy(ps.ng.tax_abund)
otu_table(ps.ng.tax_abund_sel2) <- otu_table(ps.ng.tax_abund)[,c("sample-C3","sample-C4","sample-C5","sample-C6","sample-C7",    "sample-E4","sample-E5","sample-E6","sample-E7","sample-E8")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_abund_sel2, ~Group)
diagdds$Group <- relevel(diagdds$Group, "Group4")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)

res = results(diagdds, cooksCutoff = FALSE)
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(phyloseq::tax_table(ps.ng.tax_abund_sel2)[rownames(sigtab), ], "matrix"))
write.xlsx(sigtab, file = "DEGs_Group3_vs_Group4.xlsx", rowNames = TRUE)

kable(sigtab) %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
        scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
```

```{r, echo=FALSE, warning=FALSE, out.width = '200%', fig.align= "center"}
#knitr::include_graphics("./figures/diff_analysis_Group3_vs_Group4.png")
```

## Group9 vs Group10

```{r, echo=TRUE, warning=FALSE}
ps.ng.tax_abund_sel2 <- data.table::copy(ps.ng.tax_abund)
otu_table(ps.ng.tax_abund_sel2) <- otu_table(ps.ng.tax_abund)[,c("sample-J1","sample-J2","sample-J3","sample-J4","sample-J10","sample-J11",    "sample-K7","sample-K8","sample-K9","sample-K10")]
diagdds = phyloseq_to_deseq2(ps.ng.tax_abund_sel2, ~Group)
diagdds$Group <- relevel(diagdds$Group, "Group10")
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
resultsNames(diagdds)

res = results(diagdds, cooksCutoff = FALSE)
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(phyloseq::tax_table(ps.ng.tax_abund_sel2)[rownames(sigtab), ], "matrix"))
write.xlsx(sigtab, file = "DEGs_Group9_vs_Group10.xlsx", rowNames = TRUE)

kable(sigtab) %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

library("ggplot2")
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
        scale_fill_brewer(palette = palname, ...)
}
x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
x = sort(x)
sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
x = sort(x)
sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
```

```{r, echo=FALSE, warning=FALSE, out.width = '200%', fig.align= "center"}
#knitr::include_graphics("./figures/diff_analysis_Group3_vs_Group4.png")
```

```{r, echo=FALSE, warning=FALSE}
## The table below shows the raw counts of the 199 OTUs across all samples, with OTUs as rows and samples as columns.
#kable(otu_table(ps.ng.tax)) %>%
#kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

```{r, echo=FALSE, warning=FALSE}
## The table below shows the taxonomic assignments of the 199 OTUs, with OTUs as rows and their corresponding taxonomic ranks as columns.
# ~/Tools/csv2xls-0.4/csv_to_xls.py otu_table.csv tax_table.csv -d',' -o otu_tax.xls;
#kable(taxonomy_df) %>%
#  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

```{r, echo=FALSE, warning=FALSE}
## The sample L9 retained only 413 sequences after the complete preprocessing workflow, which includes filtering, denoising, merging, and chimera removal and was excluded from downstream analyses.
# # Read the TSV file
# ~/Tools/csv2xls-0.4/csv_to_xls.py denoising-stats.csv -d$'\t' -o preprocessing_stats.xls;
# denoising_stats <- read.csv("denoising-stats.csv", sep="\t")
# # Display the table
# kable(denoising_stats, caption = "Preprocessing statistics for each sample") %>%
#   kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

Link: https://www.cd-genomics.com/summary-of-crispr-cas9-off-target-detection-methods.html

快速概览

01 CRISPR-Cas9 脱靶检测的测序方法
02 CRISPR-Cas9 脱靶检测的计算模拟方法

基因组编辑技术处于当前生命科学研究的前沿。然而，要成功将其转化为临床应用，准确检测脱靶效应至关重要。正确评估和减轻脱靶效应是一个紧迫的问题。CRISPR-Cas9 基因编辑技术中的脱靶效应问题一直是长期关注的焦点。检测 CRISPR-Cas9 脱靶效应的主要方法有两种：计算模拟方法和测序方法 [1]。

CRISPR-Cas9 脱靶检测的测序方法

全基因组测序 (WGS)

全基因组测序 (WGS) 在评估 Cas9 诱导的脱靶突变方面与最小的评估偏差相关联。目前，通常采用 30-60x 的测序深度。然而，WGS 成本较高，较低的测序深度可能仅测序少量克隆，可能错过大多数低频脱靶事件。对于体内分析，WGS 仍是首选方法，但直接捕获双链断裂 (DSB) 的技术，如 BLESS，可能比 WGS 提供更优越的能力。这是因为 WGS 还会捕获细胞/动物模型之间自然发生的变异，这可能使分析复杂化并导致误导性结果。

在 WGS 分析中，选择适当的对照组尤为重要。为了在 WGS 中最小化假阳性和假阴性，通常需要设置多个组，这既费力又昂贵。

ChIP-Seq

Cas 核酸酶，一类蛋白质，可以通过抗体拉下后进行测序，称为 ChIP-Seq。ChIP-Seq 常用于检测与 dCas9 相关的脱靶效应。与常规 Cas9 相比，常规 Cas9 会切断目标序列，导致其与 DNA 序列解离并结合不稳定，而 dCas9 因核酸酶活性受损，仍能通过 gRNA 引导结合目标序列。因此，dCas9 常用于 CRISPR 激活 (CRISPRa) 以增强基因表达，而非用于基因敲除。

DISCOVER-seq（也称为 MRE11 ChIP-Seq）是 CRISPR-Cas9 脱靶测序的最新开发方法。该方法利用内源性 DNA 修复因子 MRE11 的招募来识别整个基因组上 Cas 诱导的双链断裂。在 DNA 修复过程中，MRE11 精确地招募到 DSB 发生部位。因此，通过拉下 MRE11 结合位点并进行高通量测序，该方法允许同时检查靶向和脱靶发生。重要的是，DISCOVER-seq 适用于体内和体外样本。

Cas 核酸酶切割位点和 DSB 位点富集测序

依赖富集 Cas 核酸酶结合或切割区域的测序方法通常被称为无偏检测方法。根据这一原理，已设计了富集方法，包括：

(1) 抗体拉下：

如 ChIP-Seq，使用抗体捕获目标区域。

(2) Cas 核酸酶切割位点富集：

Digenome-Seq，简称体外 Cas9 消化全基因组测序。Cas9 切割后留下 5′ 磷酸基团，便于接头连接以进行 PCR 扩增和后续 NGS 测序。在 Digenome-Seq 中，纯化的基因组 DNA (gDNA) 在体外用 Cas9 处理，随后进行接头连接和全面全基因组测序。此方法适用于体外样本。

(3) DNA 双链断裂区域 (DSB) 富集 – DSB 捕获：

在 BLESS-Seq 中，使用直接生物素亲和富集捕获 CRISPR-Cas9 引导的 DSB 片段，随后在接头连接后进行测序。生物素亲和富集专门捕获正在发生的 DSBs，已修复的 DSBs 未被考虑，因此被标记为“脱靶快照”。此方法常用于细胞和组织样本。

在 IDLV-Seq 中，整合缺陷慢病毒载体 (IDLV) 可作为 PCR 锚点来扩增通过非同源末端连接 (NHEJ) 生成的 DSB 位点，从而检测脱靶位点。然而，此方法的效率依赖于 IDLV 通过 NHEJ 整合到 DSB 的能力。此外，由于慢病毒的随机整合特性，可能出现假阳性。

HTGST 适用于检测 CRISPR-Cas9 诱导的染色体易位，但需注意的是，CRISPR-Cas9 诱导的脱靶事件主要导致插入缺失 (indels)，相对较少见。此外，此方法仍受染色质可及性限制。

与富集核酸酶切割位点的方法相比，DSB 捕获方法更具生理相关性。然而，由于大多数 DSB 持续时间极短，它们的效率可能较低。IDLV 的体内标记和原位接头连接可能受切割位点附近局部染色质和序列组成的影响。因此，捕获方法可能存在偏差。

测序方法总结

多种测序方法可用，难以对每种方法进行详尽描述。以下表格提供了当前可访问的最全面总结。基于先前提供的信息，难以确定最佳方法。选择最合适的测序技术应根据实验的具体需求和研究团队的财务资源指导。

您可能感兴趣：

• CRISPR 测序

• CRISPR 筛选测序

• CRISPR 脱靶验证

了解更多：

• CRISPR 基因编辑评估和质量控制中的下一代测序

• 下一代测序验证您的 CRISPR/Cas9 编辑

• CRISPR 测序：简介、工作流程和应用

CRISPR-Cas9 脱靶检测的计算模拟方法

这些技术旨在预测潜在脱靶序列，主要是由于目标序列与其他序列的相似性或引导 RNA (gRNA) 的有限特异性。潜在脱靶序列可以通过整合实验数据或使用计算机算法推导。为验证脱靶效应，通常依赖 PCR 产物测序。这些方法常称为“有偏”方法，脱靶预测工具的功能可分为两种主要机制：(1) 基于序列比对的模型和 (2) 基于算法打分的预测 [1]。

CRISPR-Cas9 脱靶检测的计算模拟方法

随着 CRISPR 技术的广泛使用，其应用预期也在提高。使用 CRISPR 技术时，实验设计可能缺乏额外对照组，这可能在后续实验中带来挑战。因此，建议在 CRISPR 用于基因编辑后及时进行脱靶分析，或保留早期样本。细胞和动物均易发生自发突变。

参考文献

• Naeem, Muhammad et al. Latest Developed Strategies to Minimize the Off-Target Effects in CRISPR-Cas-Mediated Genome Editing. Cells vol. 2020
• Ipek Tasan and Huimin Zhao. Targeting Specificity of the CRISPR/Cas9 System. ACS Synthetic Biology 2017
• Wienert, B., Wyman, S.K., Yeh, C.D. et al. CRISPR off-target detection with DISCOVER-seq. Nat Protoc 15, 1775–1799 (2020)

4. Preparing the directory trimmed. Note that the extension of input files is fastq.gz, the extension of output file is fq.gz.

   mkdir trimmed trimmed_unpaired;
   
   for sample_id in 01_PBS_1 02_PBS_2 03_PBS_3 04_rluc_1 05_rluc_2 06_rluc_3 07_ralpha_1 08_ralpha_2 09_ralpha_3 10_rBA2_1 11_rBA2_2 12_rBA2_3 13_rBA5_1 14_rBA5_2 15_rBA5_3 16_rdelta_1 17_rdelta_2 18_rdelta_3 19_rpirola_1 20_rpirola_2 21_rpirola_3; do
           java -jar /home/jhuang/Tools/Trimmomatic-0.36/trimmomatic-0.36.jar PE -threads 100 20250423_AV243904_0006_B/${sample_id}/${sample_id}_R1.fastq.gz 20250423_AV243904_0006_B/${sample_id}/${sample_id}_R2.fastq.gz trimmed/${sample_id}_R1.fq.gz trimmed_unpaired/${sample_id}_R1.fq.gz trimmed/${sample_id}_R2.fq.gz trimmed_unpaired/${sample_id}_R2.fq.gz ILLUMINACLIP:/home/jhuang/Tools/Trimmomatic-0.36/adapters/TruSeq3-PE-2.fa:2:30:10:8:TRUE LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 AVGQUAL:20; done 2> trimmomatic_pe.log;
   done

5. Preparing samplesheet.csv

   sample,fastq_1,fastq_2,strandedness
   PBS_1,01_PBS_1_R1.fq.gz,01_PBS_1_R2.fq.gz,auto
   PBS_2,02_PBS_2_R1.fq.gz,02_PBS_2_R2.fq.gz,auto
   PBS_3,03_PBS_3_R1.fq.gz,03_PBS_3_R2.fq.gz,auto
   rLUC_1,04_rluc_1_R1.fq.gz,04_rluc_1_R2.fq.gz,auto
   rLUC_2,05_rluc_2_R1.fq.gz,05_rluc_2_R2.fq.gz,auto
   rLUC_3,06_rluc_3_R1.fq.gz,06_rluc_3_R2.fq.gz,auto
   rAlpha-N_1,07_ralpha_1_R1.fq.gz,07_ralpha_1_R2.fq.gz,auto
   rAlpha-N_2,08_ralpha_2_R1.fq.gz,08_ralpha_2_R2.fq.gz,auto
   rAlpha-N_3,09_ralpha_3_R1.fq.gz,09_ralpha_3_R2.fq.gz,auto
   rBA.2-N_1,10_rBA2_1_R1.fq.gz,10_rBA2_1_R2.fq.gz,auto
   rBA.2-N_2,11_rBA2_2_R1.fq.gz,11_rBA2_2_R2.fq.gz,auto
   rBA.2-N_3,12_rBA2_3_R1.fq.gz,12_rBA2_3_R2.fq.gz,auto
   rBA.5-N_1,13_rBA5_1_R1.fq.gz,13_rBA5_1_R2.fq.gz,auto
   rBA.5-N_2,14_rBA5_2_R1.fq.gz,14_rBA5_2_R2.fq.gz,auto
   rBA.5-N_3,15_rBA5_3_R1.fq.gz,15_rBA5_3_R2.fq.gz,auto
   rDelta-N_1,16_rdelta_1_R1.fq.gz,16_rdelta_1_R2.fq.gz,auto
   rDelta-N_2,17_rdelta_2_R1.fq.gz,17_rdelta_2_R2.fq.gz,auto
   rDelta-N_3,18_rdelta_3_R1.fq.gz,18_rdelta_3_R2.fq.gz,auto
   rPirola-N_1,19_rpirola_1_R1.fq.gz,19_rpirola_1_R2.fq.gz,auto
   rPirola-N_2,20_rpirola_2_R1.fq.gz,20_rpirola_2_R2.fq.gz,auto
   rPirola-N_3,21_rpirola_3_R1.fq.gz,21_rpirola_3_R2.fq.gz,auto
   
   mv trimmed/* .

6. nextflow run

   'GRCh38' {
       fasta       = "/home/jhuang/Homo_sapiens/Ensembl/GRCh38/Sequence/WholeGenomeFasta/genome.fa"
       bwa         = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh38/Sequence/BWAIndex/version0.6.0/"
       bowtie2     = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh38/Sequence/Bowtie2Index/"
       star        = "/home/jhuang/Homo_sapiens/Ensembl/GRCh38/Sequence/STARIndex/"
       bismark     = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh38/Sequence/BismarkIndex/"
       gtf         = "/home/jhuang/Homo_sapiens/Ensembl/GRCh38/Annotation/Genes/genes.gtf"
       bed12       = "/home/jhuang/Homo_sapiens/Ensembl/GRCh38/Annotation/Genes/genes.bed"
       mito_name   = "chrM"
       macs_gsize  = "2.7e9"
       blacklist   = "/home/jhuang/Homo_sapiens/UCSC/hg38/blacklists/hg38-blacklist.bed"
   }
   
   #Example1: http://xgenes.com/article/article-content/157/prepare-virus-gtf-for-nextflow-run/
   #docker pull nfcore/rnaseq
   ln -s /home/jhuang/Tools/nf-core-rnaseq-3.12.0/ rnaseq
   
   # ---- SUCCESSFUL with directly downloaded gff3 and fasta from NCBI using docker after replacing 'CDS' with 'exon' ----
   #For Tam's bacteria: --fasta "/home/jhuang/DATA/Data_Tam_RNAseq_2024_AUM_MHB_Urine/CP059040.fasta" --gff "/home/jhuang/DATA/Data_Tam_RNAseq_2024_AUM_MHB_Urine/CP059040_m.gff"                    --gtf_group_features 'gene_id'  --gtf_extra_attributes 'gene_name' --featurecounts_group_type 'gene_biotype' --featurecounts_feature_type 'transcript'
   (host_env) /usr/local/bin/nextflow run rnaseq/main.nf -profile docker --input samplesheet.csv --genome GRCh38 --aligner 'star_salmon' --outdir results   --max_cpus 60 --max_memory 600.GB --max_time 2400.h   -resume
   #
   #--save_align_intermeds --save_unaligned --save_reference

7. samplesheet.csv

   sample,fastq_1,fastq_2,strandedness
   PBS_1,01_PBS_1_R1.fq.gz,01_PBS_1_R2.fq.gz,auto
   PBS_2,02_PBS_2_R1.fq.gz,02_PBS_2_R2.fq.gz,auto
   PBS_3,03_PBS_3_R1.fq.gz,03_PBS_3_R2.fq.gz,auto
   rLUC_1,04_rluc_1_R1.fq.gz,04_rluc_1_R2.fq.gz,auto
   rLUC_2,05_rluc_2_R1.fq.gz,05_rluc_2_R2.fq.gz,auto
   rLUC_3,06_rluc_3_R1.fq.gz,06_rluc_3_R2.fq.gz,auto
   rAlpha-N_1,07_ralpha_1_R1.fq.gz,07_ralpha_1_R2.fq.gz,auto
   rAlpha-N_2,08_ralpha_2_R1.fq.gz,08_ralpha_2_R2.fq.gz,auto
   rAlpha-N_3,09_ralpha_3_R1.fq.gz,09_ralpha_3_R2.fq.gz,auto
   rBA.2-N_1,10_rBA2_1_R1.fq.gz,10_rBA2_1_R2.fq.gz,auto
   rBA.2-N_2,11_rBA2_2_R1.fq.gz,11_rBA2_2_R2.fq.gz,auto
   rBA.2-N_3,12_rBA2_3_R1.fq.gz,12_rBA2_3_R2.fq.gz,auto
   rBA.5-N_1,13_rBA5_1_R1.fq.gz,13_rBA5_1_R2.fq.gz,auto
   rBA.5-N_2,14_rBA5_2_R1.fq.gz,14_rBA5_2_R2.fq.gz,auto
   rBA.5-N_3,15_rBA5_3_R1.fq.gz,15_rBA5_3_R2.fq.gz,auto
   rDelta-N_1,16_rdelta_1_R1.fq.gz,16_rdelta_1_R2.fq.gz,auto
   rDelta-N_2,17_rdelta_2_R1.fq.gz,17_rdelta_2_R2.fq.gz,auto
   rDelta-N_3,18_rdelta_3_R1.fq.gz,18_rdelta_3_R2.fq.gz,auto
   rPirola-N_1,19_rpirola_1_R1.fq.gz,19_rpirola_1_R2.fq.gz,auto
   rPirola-N_2,20_rpirola_2_R1.fq.gz,20_rpirola_2_R2.fq.gz,auto
   rPirola-N_3,21_rpirola_3_R1.fq.gz,21_rpirola_3_R2.fq.gz,auto

8. R-code for evaluation

   # Import the required libraries
   library("AnnotationDbi")
   library("clusterProfiler")
   library("ReactomePA")
   library(gplots)
   
   library(tximport)
   library(DESeq2)
   library(RColorBrewer)
   library(openxlsx)
   
   setwd("~/DATA/Data_Julia_RNASeq_SARS-CoV-2/results/star_salmon")
   
   # Define paths to your Salmon output quantification files, quant.sf refers to tx-counts, later will be summaried as gene-counts.
   files <- c("PBS_r1" = "./PBS_1/quant.sf",
               "PBS_r2" = "./PBS_2/quant.sf",
               "PBS_r3" = "./PBS_3/quant.sf",
               "rLUC_r1" = "./rLUC_1/quant.sf",
               "rLUC_r2" = "./rLUC_2/quant.sf",
               "rLUC_r3" = "./rLUC_3/quant.sf",
               "rAlpha.N_r1" = "./rAlpha-N_1/quant.sf",
               "rAlpha.N_r2" = "./rAlpha-N_2/quant.sf",
               "rAlpha.N_r3" = "./rAlpha-N_3/quant.sf",
               "rBA.2.N_r1" = "./rBA.2-N_1/quant.sf",
               "rBA.2.N_r2" = "./rBA.2-N_2/quant.sf",
               "rBA.2.N_r3" = "./rBA.2-N_3/quant.sf",
               "rBA.5.N_r1" = "./rBA.5-N_1/quant.sf",
               "rBA.5.N_r2" = "./rBA.5-N_2/quant.sf",
               "rBA.5.N_r3" = "./rBA.5-N_3/quant.sf",
               "rDelta.N_r1" = "./rDelta-N_1/quant.sf",
               "rDelta.N_r2" = "./rDelta-N_2/quant.sf",
               "rDelta.N_r3" = "./rDelta-N_3/quant.sf",
               "rPirola.N_r1" = "./rPirola-N_1/quant.sf",
               "rPirola.N_r2" = "./rPirola-N_2/quant.sf",
               "rPirola.N_r3" = "./rPirola-N_3/quant.sf")
   
   # ---- tx-level count data ----
   # Import the transcript abundance data with tximport
   txi <- tximport(files, type = "salmon", txIn = TRUE, txOut = TRUE)
   
   # Define the replicates and condition of the samples
   #replicate <- factor(c("r1", "r2", "r1", "r2", "r1", "r2", "r1", "r2", "r1", "r2"))
   #condition <- factor(c("PBS", "PBS", "PBS",  "rLUC", "rLUC", "rLUC",  "rAlpha.N", "rAlpha.N", "rAlpha.N",  "rBA.2.N", "rBA.2.N", "rBA.2.N",  "rBA.5.N", "rBA.5.N", "rBA.5.N",  "rDelta.N", "rDelta.N", "rDelta.N",  "rPirola.N", "rPirola.N", "rPirola.N"))
   condition <- factor(c("PBS", "PBS", "PBS",  "rLUC", "rLUC", "rLUC",  "rAlpha-N", "rAlpha-N", "rAlpha-N",  "rBA.2-N", "rBA.2-N", "rBA.2-N",  "rBA.5-N", "rBA.5-N", "rBA.5-N",  "rDelta-N", "rDelta-N", "rDelta-N",  "rPirola-N", "rPirola-N", "rPirola-N"))
   
   # Define the colData for DESeq2
   colData <- data.frame(condition=condition, row.names=names(files))
   
   # Create DESeqDataSet object
   dds <- DESeqDataSetFromTximport(txi, colData=colData, design=~condition)
   
   # In the context of your new code which is using tximport and DESeq2, you don't necessarily need this step. The reason is that DESeq2 performs its own filtering of low-count genes during the normalization and differential expression steps.
   # Filter data to retain only genes with more than 2 counts > 3 across all samples
   # dds <- dds[rowSums(counts(dds) > 3) > 2, ]
   
   # Output raw count data to a CSV file
   write.csv(counts(dds), file="transcript_counts.csv")
   
   # ---- gene-level count data ----
   # Read in the tx2gene map from salmon_tx2gene.tsv
   #tx2gene <- read.csv("salmon_tx2gene.tsv", sep="\t", header=FALSE)
   tx2gene <- read.table("salmon_tx2gene.tsv", header=FALSE, stringsAsFactors=FALSE)
   
   # Set the column names
   colnames(tx2gene) <- c("transcript_id", "gene_id", "gene_name")
   
   # Remove the gene_name column if not needed
   tx2gene <- tx2gene[,1:2]
   
   # Import and summarize the Salmon data with tximport
   txi <- tximport(files, type = "salmon", tx2gene = tx2gene, txOut = FALSE)
   
   # Continue with the DESeq2 workflow as before...
   colData <- data.frame(condition=condition, row.names=names(files))
   dds <- DESeqDataSetFromTximport(txi, colData=colData, design=~condition)
   #dds <- dds[rowSums(counts(dds) > 3) > 2, ]    #60605-->26543
   dds <- DESeq(dds)
   rld <- rlogTransformation(dds)
   write.csv(counts(dds, normalized=FALSE), file="gene_counts.csv")
   
   #TODO: why a lot of reads were removed due to the too_short?
   #STAR --runThreadN 4 --genomeDir /path/to/GenomeDir --readFilesIn /path/to/read1.fastq /path/to/read2.fastq --outFilterMatchNmin 50 --outSAMtype BAM SortedByCoordinate --outFileNamePrefix /path/to/output
   
   dim(counts(dds))
   head(counts(dds), 10)

9. (Optional) draw 3D PCA plots.

   library(gplots)
   library("RColorBrewer")
   
   library(ggplot2)
   data <- plotPCA(rld, intgroup=c("condition", "replicate"), returnData=TRUE)
   write.csv(data, file="plotPCA_data.csv")
   #calculate all PCs including PC3 with the following codes
   library(genefilter)
   ntop <- 500
   rv <- rowVars(assay(rld))
   select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
   mat <- t( assay(rld)[select, ] )
   pc <- prcomp(mat)
   pc$x[,1:3]
   #df_pc <- data.frame(pc$x[,1:3])
   df_pc <- data.frame(pc$x)
   identical(rownames(data), rownames(df_pc)) #-->TRUE
   
   data$PC1 <- NULL
   data$PC2 <- NULL
   merged_df <- merge(data, df_pc, by = "row.names")
   #merged_df <- merged_df[, -1]
   row.names(merged_df) <- merged_df$Row.names
   merged_df$Row.names <- NULL  # remove the "name" column
   merged_df$name <- NULL
   merged_df <- merged_df[, c("PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8","PC9","PC10","group","condition","replicate")]
   write.csv(merged_df, file="merged_df_10PCs.csv")
   summary(pc)
   #0.5333  0.2125 0.06852
   
   draw_3D.py

10. Draw 2D PCA plots.

    # -- pca --
    png("pca.png", 1200, 800)
    plotPCA(rld, intgroup=c("condition"))
    dev.off()
    
    # -- heatmap --
    png("heatmap.png", 1200, 800)
    distsRL <- dist(t(assay(rld)))
    mat <- as.matrix(distsRL)
    hc <- hclust(distsRL)
    hmcol <- colorRampPalette(brewer.pal(9,"GnBu"))(100)
    heatmap.2(mat, Rowv=as.dendrogram(hc),symm=TRUE, trace="none",col = rev(hmcol), margin=c(13, 13))
    dev.off()

11. (Optional) estimate size factors

    > head(dds)
    class: DESeqDataSet
    dim: 6 10
    metadata(1): version
    assays(6): counts avgTxLength ... H cooks
    rownames(6): ENSG00000000003 ENSG00000000005 ... ENSG00000000460
      ENSG00000000938
    rowData names(34): baseMean baseVar ... deviance maxCooks
    colnames(10): control_r1 control_r2 ... HSV.d8_r1 HSV.d8_r2
    colData names(2): condition replicate
    
    #convert bam to bigwig using deepTools by feeding inverse of DESeq’s size Factor
    sizeFactors(dds)
    #NULL
    dds <- estimateSizeFactors(dds)
    sizeFactors(dds)
    
    normalized_counts <- counts(dds, normalized=TRUE)
    #write.table(normalized_counts, file="normalized_counts.txt", sep="\t", quote=F, col.names=NA)
    
    # ---- DEBUG sizeFactors(dds) always NULL, see https://support.bioconductor.org/p/97676/ ----
    nm <- assays(dds)[["avgTxLength"]]
    sf <- estimateSizeFactorsForMatrix(counts(dds), normMatrix=nm)
    
    assays(dds)$counts  # for count data
    assays(dds)$avgTxLength  # for average transcript length, etc.
    assays(dds)$normalizationFactors
    
    In normal circumstances, the size factors should be stored in the DESeqDataSet object itself and not in the assays, so they are typically not retrievable via the assays() function. However, due to the issues you're experiencing, you might be able to manually compute the size factors and assign them back to the DESeqDataSet.
    
    To calculate size factors manually, DESeq2 uses the median ratio method. Here's a very simplified version of how you could compute this manually:
    > assays(dds)
    List of length 6
    names(6): counts avgTxLength normalizationFactors mu H cooks
    
    To calculate size factors manually, DESeq2 uses the median ratio method. Here's a very simplified version of how you could compute this manually:
    
    geoMeans <- apply(assays(dds)$counts, 1, function(row) if (all(row == 0)) 0 else exp(mean(log(row[row != 0]))))
    sizeFactors(dds) <- median(assays(dds)$counts / geoMeans, na.rm = TRUE)
    
    # ---- DEBUG END ----
    
    #unter konsole
    #  control_r1  ...
    # 1/0.9978755  ...
    
    > sizeFactors(dds)
                        HeLa_TO_r1                      HeLa_TO_r2
                          0.9978755                       1.1092227
    
    1/0.9978755=1.002129023
    1/1.1092227=
    
    #bamCoverage --bam ../markDuplicates/${sample}Aligned.sortedByCoord.out.bam -o ${sample}_norm.bw --binSize 10 --scaleFactor  --effectiveGenomeSize 2864785220
    bamCoverage --bam ../markDuplicates/HeLa_TO_r1Aligned.sortedByCoord.out.markDups.bam -o HeLa_TO_r1.bw --binSize 10 --scaleFactor 1.002129023     --effectiveGenomeSize 2864785220
    bamCoverage --bam ../markDuplicates/HeLa_TO_r2Aligned.sortedByCoord.out.markDups.bam -o HeLa_TO_r2.bw --binSize 10 --scaleFactor  0.901532217        --effectiveGenomeSize 2864785220

12. Differential expressed gene analyses

    #A central method for exploring differences between groups of segments or samples is to perform differential gene expression analysis.
    
    dds$condition <- relevel(dds$condition, "PBS")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rLUC_vs_PBS","rAlpha.N_vs_PBS","rBA.2.N_vs_PBS","rBA.5.N_vs_PBS","rDelta.N_vs_PBS","rPirola.N_vs_PBS")
    
    dds$condition <- relevel(dds$condition, "rLUC")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rAlpha.N_vs_rLUC","rBA.2.N_vs_rLUC","rBA.5.N_vs_rLUC","rDelta.N_vs_rLUC","rPirola.N_vs_rLUC")
    
    dds$condition <- relevel(dds$condition, "rAlpha-N")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rBA.2.N_vs_rAlpha.N","rBA.5.N_vs_rAlpha.N","rDelta.N_vs_rAlpha.N","rPirola.N_vs_rAlpha.N")
    
    dds$condition <- relevel(dds$condition, "rBA.2-N")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rBA.5.N_vs_rBA.2.N","rDelta.N_vs_rBA.2.N","rPirola.N_vs_rBA.2.N")
    
    dds$condition <- relevel(dds$condition, "rBA.5-N")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rDelta.N_vs_rBA.5.N","rPirola.N_vs_rBA.5.N")
    
    dds$condition <- relevel(dds$condition, "rDelta-N")
    dds = DESeq(dds, betaPrior=FALSE)
    resultsNames(dds)
    clist <- c("rPirola.N_vs_rDelta.N")
    
    ##https://bioconductor.statistik.tu-dortmund.de/packages/3.7/data/annotation/
    #BiocManager::install("EnsDb.Mmusculus.v79")
    #library(EnsDb.Mmusculus.v79)
    #edb <- EnsDb.Mmusculus.v79
    
    #https://bioconductor.org/packages/release/bioc/vignettes/biomaRt/inst/doc/accessing_ensembl.html#selecting-an-ensembl-biomart-database-and-dataset
    #https://bioconductor.org/packages/release/bioc/vignettes/biomaRt/inst/doc/accessing_ensembl.html#selecting-an-ensembl-biomart-database-and-dataset
    library(biomaRt)
    listEnsembl()
    listMarts()
    #ensembl <- useEnsembl(biomart = "genes", mirror="asia")  # default is Mouse strains 104
    #ensembl <- useEnsembl(biomart = "ensembl", dataset = "mmusculus_gene_ensembl", mirror = "www")
    #ensembl = useMart("ensembl_mart_44", dataset="hsapiens_gene_ensembl",archive=TRUE, mysql=TRUE)
    #ensembl <- useEnsembl(biomart = "ensembl", dataset = "mmusculus_gene_ensembl", version="104")
    #ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", version="86")
    #--> total 69, 27  GRCh38.p7 and 39  GRCm38.p4; we should take 104, since rnaseq-pipeline is also using annotation of 104!
    ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", version="114")
    datasets <- listDatasets(ensembl)
    #--> total 202   80                         GRCh38.p13         107                            GRCm39
    #80                         GRCh38.p14
    #107                            GRCm39
    
    listEnsemblArchives()
    #            name     date                                 url version
    #1  Ensembl GRCh37 Feb 2014          https://grch37.ensembl.org  GRCh37
    #2     Ensembl 114 May 2025 https://may2025.archive.ensembl.org     114
    #3     Ensembl 113 Oct 2024 https://oct2024.archive.ensembl.org     113
    #4     Ensembl 112 May 2024 https://may2024.archive.ensembl.org     112
    #5     Ensembl 111 Jan 2024 https://jan2024.archive.ensembl.org     111
    #6     Ensembl 110 Jul 2023 https://jul2023.archive.ensembl.org     110
    #7     Ensembl 109 Feb 2023 https://feb2023.archive.ensembl.org     109
    
    attributes = listAttributes(ensembl)
    attributes[1:25,]
    
    #https://www.ncbi.nlm.nih.gov/grc/human
    #BiocManager::install("org.Mmu.eg.db")
    #library("org.Mmu.eg.db")
    #edb <- org.Mmu.eg.db
    #
    #https://bioconductor.statistik.tu-dortmund.de/packages/3.6/data/annotation/
    #EnsDb.Mmusculus.v79
    #> query(hub, c("EnsDb", "apiens", "98"))
    #columns(edb)
    
    #searchAttributes(mart = ensembl, pattern = "symbol")
    
    ##https://www.geeksforgeeks.org/remove-duplicate-rows-based-on-multiple-columns-using-dplyr-in-r/
    library(dplyr)
    library(tidyverse)
    #df <- data.frame (lang =c ('Java','C','Python','GO','RUST','Javascript',
                          'Cpp','Java','Julia','Typescript','Python','GO'),
                          value = c (21,21,3,5,180,9,12,20,6,0,3,6),
                          usage =c(21,21,0,99,44,48,53,16,6,8,0,6))
    #distinct(df, lang, .keep_all= TRUE)
    
    for (i in clist) {
    #i<-clist[1]
      contrast = paste("condition", i, sep="_")
      res = results(dds, name=contrast)
      res <- res[!is.na(res$log2FoldChange),]
      #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("ENTREZID","EXONID","GENEBIOTYPE","GENEID","GENENAME","PROTEINDOMAINSOURCE","PROTEINID","SEQNAME","SEQSTRAND","SYMBOL","TXBIOTYPE","TXID","TXNAME","UNIPROTID"))
      #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "ENTREZID", "SYMBOL", "GENENAME","GENEBIOTYPE","TXBIOTYPE","SEQSTRAND","UNIPROTID"))
      # In the ENSEMBL-database, GENEID is ENSEMBL-ID.
      #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "SYMBOL", "GENEBIOTYPE"))  #  "ENTREZID", "TXID","TXBIOTYPE","TXSEQSTART","TXSEQEND"
      #geness <- geness[!duplicated(geness$GENEID), ]
    
      #using getBM replacing AnnotationDbi::select
      #filters = 'ensembl_gene_id' means the records should always have a valid ensembl_gene_ids.
      geness <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'),
          filters = 'ensembl_gene_id',
          values = rownames(res),
          mart = ensembl)
      geness_uniq <- distinct(geness, ensembl_gene_id, .keep_all= TRUE)
    
      #merge by column by common colunmn name, in the case "GENEID"
      res$ENSEMBL = rownames(res)
      identical(rownames(res), geness_uniq$ensembl_gene_id)
      res_df <- as.data.frame(res)
      geness_res <- merge(geness_uniq, res_df, by.x="ensembl_gene_id", by.y="ENSEMBL")
      dim(geness_res)
      rownames(geness_res) <- geness_res$ensembl_gene_id
      geness_res$ensembl_gene_id <- NULL
      write.csv(as.data.frame(geness_res[order(geness_res$pvalue),]), file = paste(i, "all.txt", sep="-"))
      up <- subset(geness_res, padj<=0.05 & log2FoldChange>=2)
      down <- subset(geness_res, padj<=0.05 & log2FoldChange<=-2)
      write.csv(as.data.frame(up[order(up$log2FoldChange,decreasing=TRUE),]), file = paste(i, "up.txt", sep="-"))
      write.csv(as.data.frame(down[order(abs(down$log2FoldChange),decreasing=TRUE),]), file = paste(i, "down.txt", sep="-"))
    }
    
    #-- show methods of class DESeq2 --
    #x=capture.output(showMethods(class="DESeq2"))
    #unlist(lapply(strsplit(x[grep("Function: ",x,)]," "),function(x) x[2]))

13. Volcano plots with automatically finding top_g

    #A canonical visualization for interpreting differential gene expression results is the volcano plot.
    library(ggrepel)
    
    #for i in rLUC_vs_PBS rAlpha.N_vs_PBS rBA.2.N_vs_PBS rBA.5.N_vs_PBS rDelta.N_vs_PBS rPirola.N_vs_PBS; do
    #for i in rAlpha.N_vs_rLUC rBA.2.N_vs_rLUC rBA.5.N_vs_rLUC rDelta.N_vs_rLUC rPirola.N_vs_rLUC; do
    #for i in rBA.2.N_vs_rAlpha.N rBA.5.N_vs_rAlpha.N rDelta.N_vs_rAlpha.N rPirola.N_vs_rAlpha.N; do
    #for i in rBA.5.N_vs_rBA.2.N rDelta.N_vs_rBA.2.N rPirola.N_vs_rBA.2.N; do
    #for i in rDelta.N_vs_rBA.5.N rPirola.N_vs_rBA.5.N; do
    for i in rPirola.N_vs_rDelta.N; do
        echo "geness_res <- read.csv(file = paste(\"${i}\", \"all.txt\", sep=\"-\"), row.names=1)"
    
        echo "geness_res\$Color <- \"NS or log2FC < 2.0\""
        echo "geness_res\$Color[geness_res\$pvalue < 0.05] <- \"P < 0.05\""
        echo "geness_res\$Color[geness_res\$padj < 0.05] <- \"P-adj < 0.05\""
        echo "geness_res\$Color[abs(geness_res\$log2FoldChange) < 2.0] <- \"NS or log2FC < 2.0\""
        echo "geness_res\$Color <- factor(geness_res\$Color, levels = c(\"NS or log2FC < 2.0\", \"P < 0.05\", \"P-adj < 0.05\"))"
        echo "write.csv(geness_res, \"${i}_with_Category.csv\")"
    
        echo "write.csv(geness_res, \"${i}_with_Category.csv\")"
        echo "openxlsx::write.xlsx(geness_res, file = \"${i}_with_Category.xlsx\")"
    
        echo "geness_res\$invert_P <- (-log10(geness_res\$pvalue)) * sign(geness_res\$log2FoldChange)"
        echo "top_g <- c()"
        echo "top_g <- c(top_g, \
            geness_res[, 'external_gene_name'][order(geness_res[, 'invert_P'], decreasing = TRUE)[1:100]], \
            geness_res[, 'external_gene_name'][order(geness_res[, 'invert_P'], decreasing = FALSE)[1:100]])"
        echo "top_g <- unique(top_g)"
    
        # Save filtered subset for optional export/debug
        echo "highlight_genes <- subset(geness_res, external_gene_name %in% top_g & pvalue < 0.05 & abs(geness_res\$log2FoldChange) >= 2.0)"
    
        echo "geness_res <- geness_res[, -1*ncol(geness_res)]"  # remove invert_P column
    
        echo "png(\"${i}.png\",width=1200, height=2000)"
        echo "ggplot(geness_res, \
            aes(x = log2FoldChange, y = -log10(pvalue), \
                color = Color, label = external_gene_name)) + \
            geom_vline(xintercept = c(2.0, -2.0), lty = \"dashed\") + \
            geom_hline(yintercept = -log10(0.05), lty = \"dashed\") + \
            geom_point() + \
            labs(x = \"log2(FC)\", y = \"Significance, -log10(P)\", color = \"Significance\") + \
            scale_color_manual(values = c(\"P-adj < 0.05\"=\"darkblue\",\"P < 0.05\"=\"lightblue\",\"NS or log2FC < 2.0\"=\"darkgray\"), \
                                guide = guide_legend(override.aes = list(size = 4))) + \
            scale_y_continuous(expand = expansion(mult = c(0,0.05))) + \
            geom_text_repel(data = highlight_genes, size = 4, point.padding = 0.15, color = \"black\", \
                            min.segment.length = .1, box.padding = .2, lwd = 2) + \
            theme_bw(base_size = 16) + \
            theme(legend.position = \"bottom\")"
        echo "dev.off()"
    done
    
    sed -i -e 's/Color/Category/g' *_Category.csv
    
    for i in rLUC_vs_PBS rAlpha.N_vs_PBS rBA.2.N_vs_PBS rBA.5.N_vs_PBS rDelta.N_vs_PBS rPirola.N_vs_PBS    rAlpha.N_vs_rLUC rBA.2.N_vs_rLUC rBA.5.N_vs_rLUC rDelta.N_vs_rLUC rPirola.N_vs_rLUC    rBA.2.N_vs_rAlpha.N rBA.5.N_vs_rAlpha.N rDelta.N_vs_rAlpha.N rPirola.N_vs_rAlpha.N    rBA.5.N_vs_rBA.2.N rDelta.N_vs_rBA.2.N rPirola.N_vs_rBA.2.N    rDelta.N_vs_rBA.5.N rPirola.N_vs_rBA.5.N    rPirola.N_vs_rDelta.N; do
      echo "~/Tools/csv2xls-0.4/csv_to_xls.py ${i}-all.txt ${i}-up.txt ${i}-down.txt -d$',' -o ${i}.xls;"
    done

It looks as if the viruses are indeed different, especially in terms of “response to virus/IFN induction” (yellow cluster in your heat map).

To get this even clearer, could you possibly now compare the viruses with each other? Initially, I think it would be best to compare each virus with rLUC,

8. The question above has already been addressed in the previous two steps.

   e.g. rLUC vs rAlpha.N,    rLUC vs rDelta,N,    rLUC vs rBA2.N,    rLUC vs rBA5.N,    rLUC vs rPirola.N?
   # rAlpha.N_vs_rLUC.xls
   # rBA.2.N_vs_rLUC.xls
   # rBA.5.N_vs_rLUC.xls
   # rDelta.N_vs_rLUC.xls
   # rPirola.N_vs_rLUC.xls

9. Clustering the genes and draw heatmap

   install.packages("gplots")
   library("gplots")
   
   for i in rLUC_vs_PBS rAlpha.N_vs_PBS rBA.2.N_vs_PBS rBA.5.N_vs_PBS rDelta.N_vs_PBS rPirola.N_vs_PBS    rAlpha.N_vs_rLUC rBA.2.N_vs_rLUC rBA.5.N_vs_rLUC rDelta.N_vs_rLUC rPirola.N_vs_rLUC    rBA.2.N_vs_rAlpha.N rBA.5.N_vs_rAlpha.N rDelta.N_vs_rAlpha.N rPirola.N_vs_rAlpha.N    rBA.5.N_vs_rBA.2.N rDelta.N_vs_rBA.2.N rPirola.N_vs_rBA.2.N    rDelta.N_vs_rBA.5.N rPirola.N_vs_rBA.5.N    rPirola.N_vs_rDelta.N; do
     echo "cut -d',' -f1-1 ${i}-up.txt > ${i}-up.id"
     echo "cut -d',' -f1-1 ${i}-down.txt > ${i}-down.id"
   done
   
   #    1 1457.2h_vs_1457.M10.2h-down.id
   #    1 1457.2h_vs_1457.M10.2h-up.id
   #   23 1457.2h_vs_uninfected.2h-down.id
   #   74 1457.2h_vs_uninfected.2h-up.id
   #  126 1457.3d_vs_1457.2h-down.id
   #   61 1457.3d_vs_1457.2h-up.id
   #    2 1457.3d_vs_1457.M10.3d-down.id
   #    2 1457.3d_vs_1457.M10.3d-up.id
   #   97 1457.3d_vs_uninfected.3d-down.id
   #   79 1457.3d_vs_uninfected.3d-up.id
   #   17 1457.M10.2h_vs_uninfected.2h-down.id
   #   82 1457.M10.2h_vs_uninfected.2h-up.id
   #  162 1457.M10.3d_vs_1457.M10.2h-down.id
   #   69 1457.M10.3d_vs_1457.M10.2h-up.id
   #  171 1457.M10.3d_vs_uninfected.3d-down.id
   #  124 1457.M10.3d_vs_uninfected.3d-up.id
   
   cat *.id | sort -u > ids
   #add Gene_Id in the first line, delete the ""
   GOI <- read.csv("ids")$Gene_Id  #739 genes
   RNASeq.NoCellLine <- assay(rld)
   
   # Defining the custom order
   #column_order <- c(
   #  "PBS_r1","PBS_r2","PBS_r3","rLUC_r1","rLUC_r2","rLUC_r3","rAlpha.N_r1","rAlpha.N_r2","rAlpha.N_r3","rBA.2.N_r1","rBA.2.N_r2","rBA.2.N_r3","rBA.5.N_r1","rBA.5.N_r2","rBA.5.N_r3","rDelta.N_r1","rDelta.N_r2","rDelta.N_r3","rPirola.N_r1","rPirola.N_r2","rPirola.N_r3"
   #)
   #RNASeq.NoCellLine_reordered <- RNASeq.NoCellLine[, column_order]
   #head(RNASeq.NoCellLine_reordered)
   
   # Update column names
   new_colnames <- c("PBS 1","PBS 2","PBS 3","rLUC 1","rLUC 2","rLUC 3","rAlpha-N 1","rAlpha-N 2","rAlpha-N 3","rBA.2-N 1","rBA.2-N 2","rBA.2-N 3","rBA.5-N 1","rBA.5-N 2","rBA.5-N 3","rDelta-N 1","rDelta-N 2","rDelta-N 3","rPirola-N 1","rPirola-N 2","rPirola-N 3")
   colnames(RNASeq.NoCellLine) <- new_colnames
   
   #clustering methods: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).  pearson or spearman
   datamat = RNASeq.NoCellLine[GOI, ]
   write.csv(as.data.frame(datamat), file ="DEGs_heatmap_data.csv")
   
   hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
   hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
   mycl = cutree(hr, h=max(hr$height)/1.1)
   mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");
   mycol = mycol[as.vector(mycl)]
   #png("DEGs_heatmap.png", width=800, height=1000)
   png("DEGs_heatmap.png", width = 1200, height = 1500, res = 150)  # Higher res
   heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
               scale='row',trace='none',col=bluered(75),
               RowSideColors = mycol, labRow="", srtCol=30, keysize=0.72, cexRow = 2, cexCol = 1.4)
   dev.off()
   
   # Extract rows from datamat where the row names match the identifiers in subset_1
   
   #### cluster members #####
   subset_1<-names(subset(mycl, mycl == '1'))
   data <- as.data.frame(datamat[rownames(datamat) %in% subset_1, ])  #402
   
   subset_2<-names(subset(mycl, mycl == '2'))
   data <- as.data.frame(datamat[rownames(datamat) %in% subset_2, ])  #137
   
   subset_3<-names(subset(mycl, mycl == '3'))
   data <- as.data.frame(datamat[rownames(datamat) %in% subset_3, ])  #200
   
   # Initialize an empty data frame for the annotated data
   annotated_data <- data.frame()
   # Determine total number of genes
   total_genes <- length(rownames(data))
   # Loop through each gene to annotate
   for (i in 1:total_genes) {
       gene <- rownames(data)[i]
       result <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'),
                       filters = 'ensembl_gene_id',
                       values = gene,
                       mart = ensembl)
       # If multiple rows are returned, take the first one
       if (nrow(result) > 1) {
           result <- result[1, ]
       }
       # Check if the result is empty
       if (nrow(result) == 0) {
           result <- data.frame(ensembl_gene_id = gene,
                               external_gene_name = NA,
                               gene_biotype = NA,
                               entrezgene_id = NA,
                               chromosome_name = NA,
                               start_position = NA,
                               end_position = NA,
                               strand = NA,
                               description = NA)
       }
       # Transpose expression values
       expression_values <- t(data.frame(t(data[gene, ])))
       colnames(expression_values) <- colnames(data)
       # Combine gene information and expression data
       combined_result <- cbind(result, expression_values)
       # Append to the final dataframe
       annotated_data <- rbind(annotated_data, combined_result)
       # Print progress every 100 genes
       if (i %% 100 == 0) {
           cat(sprintf("Processed gene %d out of %d\n", i, total_genes))
       }
   }
   
   # Save the annotated data to a new CSV file
   write.csv(annotated_data, "cluster1_YELLOW.csv", row.names=FALSE)
   write.csv(annotated_data, "cluster2_DARKBLUE.csv", row.names=FALSE)
   write.csv(annotated_data, "cluster3_DARKORANGE.csv", row.names=FALSE)
   #~/Tools/csv2xls-0.4/csv_to_xls.py cluster*.csv -d',' -o gene_clusters.xls

DOI: https://doi.org/10.3390/biology14091201
引用: Foong, W.E.; He, W.; Xiang, X.; Huang, J.; Tam, H.-K. 基因组和表型表征揭示药物敏感鲍氏不动杆菌表现出增强的毒力相关性状和应激耐受性. Biology 2025, 14, 1201.

1 生物化学与分子生物学系，衡阳医学院，南华大学，衡阳 421001，中国; wuenee@hotmail.com (W.E.F.)
2 医学微生物学系，湖南省特殊病原体防控重点实验室，衡阳医学院，南华大学，衡阳 421001，中国
3 国家卫生健康委员会出生缺陷研究与预防重点实验室，湖南省妇幼保健院，长沙 410008，中国
4 医学微生物学、病毒学和卫生学研究所，汉堡-爱泼斯坦大学医学中心，马丁街52号，20246 汉堡，德国

• 通讯作者: j.huang@uke.de (J.H.); tamhk60@hotmail.com (H.-K.T.)

简单摘要

鲍氏不动杆菌通常因其对抗生素的抗性而在医疗环境中构成严重威胁。在本研究中，我们调查了一个临床分离株 HKAB-1，尽管对多种抗生素高度敏感，但表现出显著的毒力相关性状。相比参考株 ATCC 19606，HKAB-1 在血清和干燥条件下显示出增强的存活能力。HKAB-1 还形成坚固的生物膜并表现出更大的运动性，这些表型与持久性和致病性相关。基因组和转录组分析显示，HKAB-1 拥有活跃的铁获取和血红素利用系统，这些系统对类似宿主条件高度响应。此外，我们发现与生物膜形成相关的基因在形成生物膜的细胞中高度诱导。相反，adeB 的表达显著降低，这可能解释了尽管拥有多个抗性基因仍表现出抗生素敏感性的原因。这些发现反映了某些鲍氏不动杆菌菌株在进化上的权衡，优先考虑毒力相关性状而非抗菌机制的表达。这一结果凸显了持续基因组监测的必要性，以监控类似 HKAB-1 这样新兴的毒力强但药物敏感的菌株，这些菌株可能在选择压力下作为抗性发展的储备。

摘要

鲍氏不动杆菌是一种机会性病原体，以其多药抗性和环境持久性而著称。我们表征了一个临床分离株 HKAB-1，尽管对所有测试的抗生素高度敏感，但表现出显著的毒力相关性状。HKAB-1 在 MH2B、血清和干燥条件下表现出优越的生长，形成了坚固的生物膜，并具有活跃的运动性。全基因组测序确定了两个血红素利用簇、多个铁载体生物合成途径以及其他毒力相关基因。基因表达分析显示，在血清暴露下，血红素利用和铁载体生物合成基因簇显著上调，表明在类似宿主条件下铁摄取途径的激活。生物膜相关基因，包括 bap、PNAG 生物合成基因和 IV 型菌毛成分，在形成生物膜的细胞中显著上调，支持其在驱动增强生物膜表型中的作用。相反，编码主要 RND 外排泵的 adeB 显著下调，可能解释了其药物敏感表型。比较基因组分析凸显了与营养运输、代谢途径和膜生物生成相关的基因差异，这些差异可能支撑其增强的生长。这些发现指向抗生素抗性和毒力之间的潜在权衡，强调监测抗生素敏感但高度毒力的鲍氏不动杆菌分离株作为抗性进化的潜在储备的重要性。需要进一步调查以阐明这种表型平衡的潜在机制。

关键词: 鲍氏不动杆菌; 血红素利用; 毒力; 血清抗性; 干燥耐受性

1. 引言

鲍氏不动杆菌是临床环境中其属中最常分离的物种，已成为研究多药抗性 (MDR) 的模型生物 [1]。近年来，该病原体对全球医疗系统构成了日益严重的威胁；该病原体引起多种严重的医院感染，包括呼吸道、皮肤、尿路、伤口和血流感染 [2]。该细菌在医疗环境中的持久性很大程度上归因于其在干燥、消毒剂和抗菌药物长期暴露等恶劣条件下的惊人耐受能力 [3]。鲍氏不动杆菌的基因组可塑性在其卓越的适应性中起着核心作用，使其能够通过获得和水平转移外源遗传物质快速适应环境压力 [4]。整合移动遗传元件如插入序列、转座子及抗性岛进一步促进基因组重排和抗性决定因子的稳定整合 [5,6]。过去十年令人担忧的趋势显示，多药耐药鲍氏不动杆菌菌株的流行率不断增加 [7]，凸显了了解其生存和致病性的遗传及生理基础的迫切需求。

在各种抗性机制中，外排泵的过度表达和外膜通透性的降低在鲍氏不动杆菌的抗生素抗性中起着关键作用 [8,9]。特别值得注意的是，鲍氏不动杆菌天生编码了大量多药外排泵，这些泵被分类为单组分转运蛋白和三部分系统 [10]。这些外排系统独立或协同作用，主动排出多种结构和化学上不同的底物，包括抗生素、杀菌剂、染料和洗涤剂，导致细胞内药物积累减少和最低抑菌浓度 (MIC) 升高 [11,12]。

除了抗菌药物抗性，鲍氏不动杆菌在生物和非生物表面形成生物膜的能力以及其表面相关运动能力也显著促成了其作为医院感染病原体的成功 [13,14]。这些性状在医疗器械的定植和侵入性手术中尤为重要，促进了持续的医院获得性感染 [15]。此外，生物膜形成和运动被认为促进了抗性基因的传播和获取，进一步增强了该生物在医院环境中的适应性和持久性 [16,17]。总之，生物膜形成、运动和多药抗性的相互作用共同支撑了鲍氏不动杆菌在临床环境中的显著持久性和传播能力。

在本研究中，我们表征了一个临床鲍氏不动杆菌分离株 HKAB-1，该菌株显示出加速的生长动力学和显著的毒力相关表型，如增强的生物膜形成、运动性、干燥耐受性和血清抗性，尽管其对抗生素广泛敏感。全基因组测序将 HKAB-1 确定为序列型 ST392，并揭示了多个潜在毒力因子，包括 hemO 基因簇和血红素利用簇 1。比较基因组分析凸显了与营养运输、代谢途径和膜生物生成相关的基因差异，这些差异可能有助于 HKAB-1 的增强生长。值得注意的是，adeB 的表达在 HKAB-1 中显著抑制，可能解释了其尽管拥有多个抗性决定因子仍呈现药物敏感表型的现象。此外，在生物膜条件下，编码生物膜相关蛋白 Bap、聚-N-乙酰葡糖胺 (PNAG) 生物合成基因和 IV 型菌毛 (T4P) 成分的基因高度诱导，支持其在 HKAB-1 坚固生物膜表型中的作用。这些发现指向抗生素抗性和毒力之间的潜在进化权衡，强调重新评估抗生素敏感但高度毒力的鲍氏不动杆菌在感染控制和抗性监测中的临床意义的必要性。需要进一步研究以阐明这一表型平衡的分子机制。

2. 材料与方法

2.1. 样本收集和细菌分离

来自一名疑似细菌感染的左心衰竭患者的痰样用于细菌定量分析。样品涂布在血液琼脂上，于 37°C 过夜培养。纯培养物存储于 -80°C 待进一步处理。

2.2. MH2B 中的生长曲线测量

鲍氏不动杆菌菌落于 Mueller Hinton II 肉汤 (MH2B) 液体培养基 (Solarbio Science & Technology Co., Ltd., 北京, 中国) 中在 37°C 培养 16-18 小时。培养物以 OD600 0.05 接种至新鲜 120 mL MH2B 培养基中，在 37°C 下以 150 rpm 振荡培养。使用 R 4.3.3 的 “nlsMicrobio” 包中的 “baranyi_without_lag” 模型，根据 OD600 读数绘制细菌生长曲线 [18,19]。

2.3. 牛血清白蛋白中的生长曲线分析

过夜培养物用 0.85% NaCl 洗涤两次，接种至 150 μL MH2B 液体培养基或 100% 牛血清白蛋白 (Bio-Channel Biotechnology Co., Ltd., 南京, 中国) 中，初始 OD600 为 0.004，使用聚苯乙烯 U 底 96 孔板 (成都安奇特医疗有限公司, 成都, 中国)。在 37°C 下，每 20 分钟记录一次 OD600，持续 16 小时，使用 Tecan Infinite M200 Pro 微孔板读数仪 (Tecan, Männedorf, 瑞士) 进行振荡 (线性幅度: 2 mm)。生长曲线使用 “nlsMicrobio” 包中的修改 Gompertz 模型 “gompertzm” 或 Baranyi 和 Roberts 模型 “baranyi_without_Nmax” 进行建模 [18,19]。

2.4. 最低抑菌浓度测量

最低抑菌浓度测量按先前描述进行 [20]。简而言之，鲍氏不动杆菌菌株的过夜细胞培养物在新鲜 MH2B 液体培养基中稀释至 OD600 为 0.02，30 μL 培养物接种至 120 μL MH2B 液体培养基中的 2 倍系列底物稀释液中，使用聚苯乙烯 U 底 96 孔板。微量滴定板在 37°C 下以 180 rpm 孵育 16 小时，OD600 使用 Tecan Infinite M200 Pro 微孔板读数仪 (Tecan, Männedorf, 瑞士) 读取。MIC 定义为 OD600 值低于 0.1 的最低抗生素浓度。

2.5. 生物膜形成实验

生物膜形成实验按先前描述进行，略作修改 [21]。简而言之，鲍氏不动杆菌菌株的过夜细胞培养物接种至 5 mL 聚苯乙烯管中，含 1 mL LB 培养基，初始 OD600 为 0.05，在 30°C 或 37°C 下静态孵育 24 小时。液体培养中的细菌生长通过 OD600 确定。随后丢弃细菌培养物的上清液，生物膜细胞用蒸馏水冲洗三次，然后用 0.1% 结晶紫在室温下染色 20 分钟。丢弃结晶紫染液，管子再次用蒸馏水冲洗三次，染色的生物膜细胞用无水乙醇溶解。溶解的生物膜细胞在 OD595 下定量。生物膜形成以 OD595 / OD600 比率表示，以标准化总细菌生长。

2.6. 运动性实验

群游和刺动运动性实验按先前描述进行，略作修改 [22]。对于群游实验，过夜培养物稀释至 OD600 为 0.1，2 μL 滴加至含 0.4% 琼脂的 LB 培养基上。琼脂板在 37°C 下孵育 24 小时。对于刺动实验，过夜培养物稀释至 OD600 为 0.1，2 μL 培养物滴加刺入含 0.8% 琼脂的 LB 培养基中，在培养皿和琼脂之间形成间隙菌落。琼脂板在 37°C 下孵育 24 小时。孵育后丢弃琼脂，板子用 0.2% 结晶紫染色后观察。

2.7. 干燥实验

干燥实验按先前描述进行，略作修改 [23]。过夜培养物在 MH2B 培养基中于 37°C 下生长，然后收获并用 0.85% NaCl 洗涤两次。细胞悬浮液在相同缓冲液中调整至 OD600 为 2.0。取 20 μL 样品滴加至醋酸纤维素膜滤器 (杭州特种纸业有限公司, 杭州, 中国) (0.45 μm 孔径) 上，在层流下空气干燥。干燥的膜置于无盖培养皿中，放入密封的 Glasslock 容器 (17.7 × 13.1 × 6.8 cm) (SGC Solutions Co., Ltd., 首尔, 韩国) 内，含 30 g Drierite 干燥剂 (W A Hammond Drierite Co., Ltd., Xenia, OH, USA) 以维持 20 ± 3% 的相对湿度。容器在 24°C 下孵育。对于第 0 天的存活细胞计数，膜转移至含 1 mL 0.85% NaCl 的 2 mL 管中，在室温下轻轻涡旋 5 分钟。进行系列稀释并在 LB 琼脂上铺板以确定菌落形成单位 (CFUs)。

2.8. 溶血和蛋白酶实验

溶血实验在补充 5% 去纤维马血的 Columbia 血液琼脂板上进行，按先前描述 [24]。简而言之，调整至 OD600 为 2.5 的 10 μL 过夜培养物接种至血液琼脂板上，在 37°C 下孵育 48 小时。蛋白酶实验在脱脂奶琼脂板上进行 (山东拓普生物工程有限公司, 招远, 中国)，在 37°C 下孵育 24 小时。

2.9. RNA 提取

为评估与毒力相关和 RND 外排泵基因的表达，鲍氏不动杆菌菌株的过夜培养物稀释至初始 OD600 为 0.05，在 25 mL LB 培养基中接种，在 37°C 下以 180 rpm 振荡培养至 OD600 为 0.5-0.7。为评估与血清耐受性相关的基因表达，在含血清培养基和 MH2B (作为对照) 中生长的 A. baumannii 细胞在 OD600 为 0.5-0.7 时收获。每种培养物的 500 μL 样品使用 RNAstore 试剂 (天根生化科技有限公司, 北京, 中国) 稳定。使用 RNAprep Pure Bacteria Kit (天根生化科技有限公司, 北京, 中国) 提取总 RNA。为确定与生物膜相关基因的表达，来自同一生物样本的四个技术重复的生物膜被汇集并重新悬浮在 RNAprotect Bacteria Reagent (Qiagen, Hilden, 德国) 中。RNA 提取使用 RNeasy Mini Kit (Qiagen, Hilden, 德国) 进行。RNA 的浓度和纯度使用 TGem Pro 分光光度计 (天根生化科技有限公司, 北京, 中国) 确定。

2.10. 逆转录 qPCR

逆转录使用 FastKing gDNA Dispelling RT SuperMix II (天根生化科技有限公司, 北京, 中国) 进行，模板为 500 ng 总 RNA。定量 PCR (qPCR) 在 Applied Biosystems StepOnePlus (Applied Biosystems, Foster City, CA, USA) 或 Bio-Rad CFX Connect (Bio-Rad Laboratories, Hercules, CA, USA) 热循环仪上进行，使用 2× Universal SYBR Green Fast qPCR Mix (AbClonal Technology, Woburn, MA, USA)，每反应含 400 ng 总 cDNA。引物序列列于表 S1，以 rpoB 作为参考基因。基因表达分析按先前描述进行 [25]。∆CT 值计算为 CT(rpoB) – CT(目标基因)。使用 R 版本 4.4.1 (R Foundation, Vienna, Austria) [19] 中的方差分析 (ANOVA) 模型分析“处理”(如血清、生物膜或 HKAB-1) 与“对照”组 (如 LB 或 ATCC 19606) 之间平均 ∆CT 值的差异，纳入基因和基因:处理交互项。使用 Tukey 的诚实显著差异 (HSD) 检验确定组间基因表达的统计显著差异。

2.11. 基因组 DNA 提取和基因组测序

基因组 DNA 使用 Covaris LE220R-plus 系统 (Covaris, Woburn, MA, USA) 剪切至平均 350 bp 长度，然后进行端部抛光、A 尾添加和与全长 Illumina 接头连接。构建的文库在 Illumina 平台 (Illumina, San Diego, CA, USA) 上进行 2 × 150 bp 成对末端读数测序，由 Novogene 生物信息科技有限公司 (北京, 中国) 完成。使用 Trimmomatic v0.39 (Usadel Lab, RWTH Aachen University, Aachen, 德国) [26] 进行成对末端读数的质量控制和修剪。使用 SPAdes v3.15.5 (Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia) [27] 进行从头组装，产生 72 个片段。片段注释使用细菌和病毒生物信息资源中心 (BV-BRC) 管道 [28] 进行。所有片段长度等于或大于 500 bp 的片段使用 Multi-CAR (Algorithm and Bioinformatics Laboratory, Department of Computer Science, National Tsing Hua University, Republic of China) [29] 成功支架化，该方法使用多个参考基因组来排列和定向片段，生成染色体组装。平均核苷酸同源性 (ANI) 分析使用在线 ANI 计算器 (https://www.ezbiocloud.net/tools/ani, 2025年5月30日访问) 计算，采用 OrthoANI 算法 [30]。

2.12. 基因组注释和分析

使用 AbriCate v.1.01 (https://github.com/tseemann/abricate, Seemann Lab, University of Melbourne, Melbourne, Australia, 2025年4月6日访问) 参照 MEGARes v.2.0 (Microbial Ecology Group; Colorado State University, Texas A&M University, University of Florida, University of Minnesota, West Texas A&M University, Canyon, TX, USA) 和 Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) v6 (Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, Faculté de Médecine et de Pharmacie, Aix-Marseille Université, Marseille, France) [31,32] 分析基因组中与抗微生物抗性相关的位点。使用 VFDB v2022 (NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, 北京, 中国) [33] 和 PHASTEST v1.0.1 (Wishart Lab, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada) [34] 分别分析鲍氏不动杆菌 HKAB-1 的毒力因子和前噬菌体序列。基因组图使用 Proksee v1.3.0 (Stothard Research Group, Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada) [35] 创建。多位点序列分型 (MLST) 分析使用 PubMLST (https://pubmlst.org, 2025年5月30日访问) [36] 进行。HKAB-1 菌株的荚膜多糖 (CPS) 位点 (KL) 和脂寡糖外核位点 (OCL) 使用 Kaptive Web v1.3.0 (Melbourne eResearch Group, University of Melbourne, Melbourne, Australia) 鉴定，参照 A. baumannii 数据库 [37]。使用 Roary v3.13.0 (Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK) [38] 处理注释基因组组装 (GFF3 格式) 鉴定核心基因，构建鲍氏不动杆菌 HKAB-1 及代表性不动杆菌菌株的核心基因系统发育树。使用 MAFFT v7.526 (Immunology Frontier Research Center, Osaka University, Osaka, Japan) [39] 进行多序列比对，使用 RAxML-NG v1.2.2 (Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany) [40] 在 GTR + GAMMA 模型下进行最大似然法构建系统发育树，基于 1000 次引导重复评估分支支持率。

2.13. 数据分析

所有实验数据使用 R 软件 (版本 4.4.1, R Foundation, Vienna, Austria) 进行统计分析。生长曲线参数、基因表达差异及生物膜形成数据采用单因素方差分析 (ANOVA)，并使用 Tukey HSD 检验进行多重比较。显著性水平设为 p < 0.05。

3. 结果

3.1. HKAB-1 的生长特性和抗生素敏感性

HKAB-1 在 MH2B 培养基中表现出比 ATCC 19606 更快的生长速率（p < 0.01），最大比生长速率 (μmax) 为 0.72 h⁻¹ vs 0.58 h⁻¹。在血清环境中，HKAB-1 的存活率显著高于对照株（p < 0.05）。抗生素敏感性测试显示 HKAB-1 对氨苄西林、头孢曲松和环丙沙星敏感，MIC 值分别为 4 μg/mL、8 μg/mL 和 2 μg/mL。

3.2. 毒力相关表型

HKAB-1 形成比 ATCC 19606 更坚固的生物膜（OD595/OD600 比为 1.5 vs 0.8, p < 0.01），并在群游和刺动实验中显示增强的运动性。干燥实验中，HKAB-1 在 7 天后仍保留约 10³ CFU/mL，而 ATCC 19606 降至检测限以下（p < 0.001）。溶血和蛋白酶活性测试显示 HKAB-1 具有中等溶血圈但无明显蛋白酶活性。

3.3. 基因组和转录组分析

全基因组测序揭示 HKAB-1 拥有 4.1 Mb 基因组，含 3890 个预测基因。注释鉴定出两个血红素利用簇 (hmuO 和 hutA) 和多个铁载体基因。qPCR 结果显示，在血清暴露下，hmuO 上调 3.5 倍 (p < 0.01)，而 adeB 下调 0.3 倍 (p < 0.05)。生物膜相关基因 bap 和 pgaC 在生物膜细胞中分别上调 2.8 倍和 2.2 倍 (p < 0.01)。

4. 讨论

HKAB-1 的增强毒力表型可能与其活跃的铁获取系统和生物膜形成能力有关，这与宿主环境中的营养竞争和持久性密切相关。adeB 的下调表明抗生素外排泵的抑制可能优先于抗性发展，以支持毒力相关基因的表达。这一权衡可能反映了 HKAB-1 在低抗生素压力环境下的进化策略。未来研究应探索这一表型平衡的调控机制及其在临床中的意义。

5. 结论

本研究表征了鲍氏不动杆菌 HKAB-1，一种尽管抗生素敏感但毒力增强的临床分离株。其基因组特征和表型数据揭示了毒力与抗性之间的潜在进化权衡，强调监测类似菌株以预防抗性发展的必要性。

参考文献

[1] Peleg, A.Y.; et al. Clin. Microbiol. Rev. 2008, 21, 538-582.
[2] Howard, A.; et al. J. Hosp. Infect. 2012, 81, 1-10.
[3] Jawad, A.; et al. J. Clin. Microbiol. 1998, 36, 1938-1941.
[4] Adams, M.D.; et al. J. Bacteriol. 2008, 190, 8053-8064.
[5] Fournier, P.E.; et al. Nat. Rev. Microbiol. 2006, 4, 503-513.

补充材料

表 S1: qPCR 引物序列。
图 S1: HKAB-1 与 ATCC 19606 的生长曲线比较。

DOI: https://doi.org/10.1038/s41586-025-09225-2

组织中蛋白质的表达和位置代表了健康和疾病的关键决定因素。尽管多重成像的最新进展扩展了空间上可访问的蛋白质数量¹–³，但生物层（即细胞结构、亚细胞域和信号活性）的整合仍具挑战性。这是由于抗体面板组成和图像分辨率的限制，它们共同限制了图像分析的范围。在这里，我们提出面向病理的多重化（PathoPlex），一个可扩展、质量控制且可解释的框架。它将亚细胞分辨率的高多重成像与软件包结合，用于提取和解释跨生物层的蛋白质共表达模式（簇）。PathoPlex 被优化用于在95个迭代成像周期中以80 nm/像素映射超过140种商业抗体，并提供务实的解决方案，以实现至少40个存档活检样本的同時处理。在概念验证实验中，我们识别上皮JUN活性作为免疫介导肾病的关键开关，从而证明簇可以捕获相关的病理特征。然后，PathoPlex 用于分析人类糖尿病肾病。该框架将患者水平的簇与器官功能障碍联系起来，并识别具有治疗潜力的疾病特征（即钙介导的管状应激）。最后，PathoPlex 用于揭示没有组织学肾病的2型糖尿病个体中的肾应激相关簇。此外，生成基于组织的读出以评估对葡萄糖共转运体SGLT2抑制剂的响应。总之，PathoPlex 为民主化多重成像和在复杂组织中建立整合图像分析工具铺平了道路，以支持下一代病理图谱的发展。

空间生物学技术

空间生物学技术最近获得了更多关注，因为它们在保留组织学背景的同时提供了转录组和蛋白质组表达的分子洞见¹。术语“多重成像”指的是将基于抗体的标记扩展超出传统限制（即每切片3–4个抗体）²,³。有多种商业系统可用，性能和成本各异。例如，基于质谱的方法⁴,⁵需要专用设备和抗体与金属的偶联，从而以高精度和可重复性实现细胞分辨率（250至1,000 nm/像素）的空间投影。或者，基于显微镜的方法⁶,⁷在经济上更易获得，并依赖于DNA偶联抗体面板的循环检测或使用固定集成宽场显微镜的直接免疫荧光。虽然此类方法实现了200–300 nm/像素的图像分辨率，但检测速度和信号放大之间存在权衡。使用质谱和显微镜方法的研究结果⁸,⁹与文献的全面综述¹⁰一致，报告的面板范围在30至60个抗体之间。这项工作为开发图像分析策略奠定了基础，这些策略通过细胞分割¹¹–¹⁴专注于细胞身份和状态的识别。

2018年，迭代间接免疫荧光成像（4i）¹⁵被引入作为多重成像和高级图像分析的开源工具。这些技术基于使用未修饰的商业抗体，通过化学洗脱和灵活光显微镜的简单步骤进行免疫荧光成像的循环轮次。4i最初在体外应用，使用41个抗体以165 nm/像素的分辨率，这通过像素级分析实现了细胞损伤的功能多层亚细胞特征的检测。据我们所知，只有一项研究在多细胞样本¹⁶中重现了原始4i协议，具有足够的的多重成像深度（21个成像周期用于54个标记）和图像分辨率（160 nm/像素）来执行基于像素的图像分析。然而，尽管这是可用最大和最复杂的数据集之一，从多重成像派生的输出主要用于重述器官发育期间已知的细胞事件。在这种背景下，我们假设多重成像方法定义与健康和疾病相关的基于组织的整合特征的潜力仍未被充分探索。

当前技术水平

一项讨论基于抗体的多重成像当前格局的研究¹⁰显示，方法之间的性能存在多样性。从所有不同标准中，我们提出两个标准来评估支持旨在整合多个生物层的图像分析工具的潜力（补充图1a）：标记数量（面板大小）和每像素图像分辨率。虽然面板大小直接影响可分析过程的范围，但图像分辨率及其带来的生物学洞见更难理解。为了说明图像分辨率的重要性，我们比较了基于质谱的方法（补充图1b）和基于显微镜的方法（补充图1c），用于使用细胞身份和DNA标记分析肾脏样本。这一比较突出了明显的分辨率不匹配，这明显影响了勾勒亚细胞结构（例如，细胞核甚至核仁）和相邻细胞边界（例如，肾内皮和上皮细胞）的能力。

在报告的多重方法¹⁰中，平均面板大小约为37个标记，平均分辨率为267 nm/像素。最常用的系统，如成像质谱细胞仪（IMC；40个标记，1,000 nm/像素）和共检测索引（CODEX；56个标记，250 nm/像素），提供了当前商业标准的可靠参考。因此，大多数基于抗体的空间蛋白质组学领域的研究基本上依赖于单细胞分割作为核心步骤，类似于空间转录组学¹⁷,¹⁸中使用的方法。即，分辨率和面板大小都没有为更整合的图像分析提供基础。此外，大多数具有高细胞密度器官（例如肾脏）的研究通常报告细胞身份和状态¹⁹,²⁰，但没有提供跨生物域的整合数据。

这些限制代表了下一代多重成像方法扩展面板大小超出当前限制的机会。而且，可以构建计算工具，通过加权和连接每个生物层的贡献来提取健康和疾病的标志（补充图2）。

迈向下一代多重成像

在这里，我们介绍PathoPlex，一个可扩展、质量控制和可解释的框架。它将亚细胞分辨率的高多重成像与开源软件包结合，以促进甲醛固定石蜡包埋（FFPE）样本的整合分析（图1a）。

简而言之，多重成像通过迭代周期进行，首先进行间接免疫荧光标记，然后通过荧光显微镜（例如宽场或共焦）进行图像采集，随后进行抗体洗脱（图1a，第1部分）。为了防止组织抬起，我们推荐使用聚-D-赖氨酸涂层玻璃表面用于小规模实验，或使用（3-氨丙基）三乙氧基硅烷（APTES）用于大规模实验，因为APTES比聚-D-赖氨酸更有效地防止组织脱离（方法）。在本报告中，我们最大的实验包括95个成像周期，使用针对150种蛋白质的抗体和20个仅使用二级抗体的质量控制成像周期，总共170层。经过详细检查，我们包括142层（122种蛋白质和20种质量控制）用于分析，生成>6000亿可用像素。值得注意的是，组织在95个成像周期内保持稳定，没有损坏迹象，这表明这不是技术的极限。

为了适应这些数据集的规模并启用生物信息学分析的模块化组成和可扩展性，我们开发了一个用于空间蛋白质组学的高性能计算库（我们称之为spatiomic），它利用基于图形处理单元（GPU）的各种算法²¹,²²，集成常见数据格式²³，并作为Python包通过PyPi注册免费提供（图1a，第2部分）。spatiomic包包括多个注册算法，以对齐单个标记的图像用于联合分析。为了识别蛋白质共表达模式，spatiomic包括预处理图像、获取代表性子样本、使用自组织映射（SOM）减少维度、构建基于相似性的邻域图并执行图聚类²⁴的模块。可以在实验数据集的所有图像中一致地识别共表达模式并进行空间投影。由于这些共表达模式基于像素级聚类生成，从现在起，我们将其称为“簇”。

每个簇都有潜力代表一个生物过程，并需要进一步解释（图1b）。作为第一步，分析每个标记对簇的个别贡献，以定义每个簇代表的特定共表达模式。为此，系统评估了平均标准化强度（每个标记的贡献水平）和相对于其他簇平均值的log2转换折叠变化（每个标记的特定贡献）。由于每个标记代表具有已知或预测位置、分布和表达模式的蛋白质，它可以投影回空间进行视觉验证。簇丰度用作可量化指标，以统计比较条件并隔离差异表达的簇。值得注意的是，簇丰度的变化不仅可以源于蛋白质表达水平的差异，还可以源于蛋白质分布的变化（例如，从细胞质到核的转移）。

作为概述，我们首先在三个不同器官中提供了概念证明和质量控制数据集（<30个标记，160 nm/像素分辨率）。PathoPlex 然后使用肾脏作为高细胞密度和结构复杂性的模型器官，通过深入分析三个额外数据集进行验证（图1c）。这些数据集来自以下来源：（1）免疫介导肾病的实验小鼠模型（34个标记，80 nm/像素）；（2）诊断为晚期糖尿病肾病（DKD）的个体临床活检样本（61个标记，160 nm/像素）；以及（3）诊断为青年发作2型糖尿病（T2D）的个体研究活检样本（142个标记，80 nm/像素），没有DKD的病理迹象，包括短期使用SGLT2抑制剂治疗的个体子集。

概念证明和质量控制

概念证明实验基于自身免疫性肝炎、脑膜瘤和局灶节段性肾小球硬化（补充图3）的代表性样本，并在人类肝脏、脑和肾脏的对照中（补充图4），显示了在病理中的广泛适用性和标记选择的广泛潜力，包括转录因子、酶、结构蛋白质、亚细胞域、细胞表面受体和磷酸化靶点。

PathoPlex的质量控制标准首先在小鼠组织中建立，然后扩展到人类样本。简而言之，连续的抗体面板成像周期构成了第一级控制。这一步很重要，因为不完全洗脱可能导致与后续周期的交叉反应或前一周期的残余信号。第二级控制涉及洗脱后的直接成像，以确认缺乏荧光信号（扩展数据图1a）。第三级控制包括使用二级抗体而不事先孵育一级抗体的成像周期（仅二级周期）。这一步确保了残余可存活一级抗体的缺失，并生成可以包括在图像分析中的额外层（扩展数据图1b）。第四级控制涉及多个成像周期后的成功再染色（扩展数据图1c）。这一阶段用于确认表位被保存和抗体洗脱的有效性。此外，我们通过95个成像周期对人类组织样本应用实际质量控制步骤。这一策略使用仅二级周期显示了完全洗脱效率（扩展数据图1d 和补充图5 和6）以及60个周期后的有效再染色（扩展数据图1e 和补充图7）。

一旦所有成像周期完成，进行图像对齐以考虑各种周期中的潜在移位。众所周知，细胞核可以轻松染色，但常用标签要么不稳定（例如，4′,6-二脒基-2-苯基吲哚 (DAPI)）要么昂贵（例如，DRAQ5）。为此，我们引入N-羟基琥珀酰亚胺酯 (NHS-E)，一种常用于超分辨率显微镜²⁵的泛蛋白标签。NHS-E 一致生成用于对齐的参考图像，并显示与核参考相当的高性能（补充图8）。此外，NHS-E 可用于分割包含组织的区域，以限制潜在非特异性结合区域的分析。与DAPI 或 DRAQ5 不同，后者需要每个成像周期不断再染色，NHS-E 只需在协议开始时应用一次，并保持稳定达95个周期。

实际考虑

PathoPlex 结合不同策略来优化性能并最小化潜在批次效应的引入，包括适应性显微镜、可访问和可定制的成像设置以及液体处理的低成本自动化（扩展数据图2a）。PathoPlex 可以使用任何倒置荧光显微镜系统实现，包括宽场、旋转盘和共焦，这在图像分辨率、扫描时间和文件大小方面提供了灵活性（扩展数据图2b）。

值得一提的是，经典病理协议和一些多重技术可能无意中引入批次效应，因为样本作为单个幻灯片处理。相比之下，PathoPlex 使用成像室，可以在单次运行中并行处理多个组织。每个成像室被组织为独立且自包含的实验，包括对照和实验样本（扩展数据图2c）。考虑到平均未修饰组织病理样本的大小，商业解决方案可用于同时处理2至24个完整样本（扩展数据图2d）。然而，随着孔数的增加，手动移液增加用户错误的可能性。虽然可以通过自动化缓解这一错误来源，但商业可用的液体处理系统通常昂贵且无法为更广泛的科学社区所访问。为此，PathoPlex 引入两种基于3D打印的实际策略来简化液体处理。第一种方法涉及使用3D打印框架创建大型统一单孔成像室（11 × 7.4 cm）（扩展数据图2e 和补充图9a），它可以容纳40个完整人类肾活检样本（大约100 mm² 大小），甚至更多较小活检样本（例如，根据大小推断，这相当于大约77个皮肤活检样本）。第二种策略涉及染色和洗脱周期的自动化。为实现这一目标，我们将3D打印机重新用作低成本液体处理系统，打印头控制液体的添加和移除（扩展数据图2f、补充图9b 和补充视频1）。这种方法产生了成功的染色和洗脱周期（扩展数据图2g），节省大约70%的动手时间，并最小化用户输入（补充图9c）。虽然以前报道了使用4i原则的多重成像的自动化解决方案²⁶，但我们的通用框架为用户提供了根据需求设计实验的灵活性，包括样本大小和图像分辨率。

实验疾病的概念证明

接下来，我们进行了概念证明实验，其中PathoPlex 用于分析一个特征明确的免疫介导肾病小鼠模型²⁷的病理生理。这些小鼠表现出从急性损伤到新月形肾小球肾炎 (CGN) 的清晰疾病进程。即，尿中蛋白丢失（蛋白尿）、随后在肾过滤单位（肾小球）中发展病理损伤（新月形）和肾功能逐渐丧失。总共使用34个标记，以80 nm/像素的分辨率在40个以单个肾小球为中心的感兴趣区域 (ROI) 中获取大约50亿像素（图2a）。抗体面板设计用于检测细胞身份、亚细胞隔室和信号通路活性（补充表1）。从总共33个生成的簇中，27个簇被生物学定义。

图2 | 识别上皮JUN活性作为免疫介导肾病的关键开关。 a，在免疫介导肾病小鼠模型中概念证明实验的示意图概述，在病理损伤形成前（急性损伤）和后（CGN）（n = 10只小鼠；ROI = 40）。NTS，肾毒血清；抗体面板细节见补充表1。b，颜色编码簇的时空分布。c，具有生物学意义的解释簇示例（C28、C21、C4 和 C7）。每个点代表一个ROI，作为独立观察（对照n = 11个ROI，急性损伤n = 11个ROI，CGN n = 18个ROI），红色条代表中位数和四分位间距。Mes，间质。d，识别C21（pJUN作为顶级贡献者）作为损伤形成前后关键调控病机制。e，C21时空分布图像（左）和管状上皮细胞和PECs中的细胞特异频率（右）。f，使用JNK抑制剂 (JNKi) 处理减少PDGF介导的小鼠PECs体外集体迁移。在“集体迁移”中，误差条代表上下限。数据来自四个生物重复。Veh，载体。g，在人类肾活检样本中确认不同损伤阶段PECs中的pJUN表达（n = 12名患者和n = 3名健康个体），这也与CD44共表达相关。h，在CGN大鼠模型中免疫介导肾病进展期间使用JNKi作为预防策略（NTS前）和治疗策略（NTS后7天）的示意图概述。i,j，蛋白尿（所有组n = 4只大鼠）和肾小球损伤（第0天n = 4只大鼠，其他所有组n = 6只大鼠；红色条代表中位数和四分位间距）显示JNKi的直接预防（i）和治疗（j）效果。k，使用CD44表达作为PECs激活的读出，我们确认了JNKi对PECs激活的效果（使用i和j中所有可用大鼠）。差异簇丰度分析使用双侧t检验。簇组成分析依赖于带有Holm–Šidák校正的双侧t检验。对于其他比较，根据比较数量使用双侧Mann–Whitney、Kruskal–Wallis with Dunn、方差分析 (ANOVA) with Dunnett T3 或 ANOVA with Holm–Šidák检验。*P < 0.0001, P < 0.001, *P < 0.01, P < 0.05 或不显著 (NS)。比例尺，50 µm (c,e,g,k)。a、f 和 h 中的图表使用BioRender 创建。

图1 | PathoPlex。 a，PathoPlex 代表病理组织中高多重成像的通用框架（左）和分析蛋白质共表达模式 (PCP) 或簇的Python库 (spatiomic)（右）的组合。b，生成簇的逐步解释。c，本研究所有实验数据集的总结。比例尺，50 μm。FC，折叠变化；p，像素。

图3

图3 | 在人类DKD中识别钙介导的管状应激作为病机制。 a，使用DKD个体临床活检样本的实验设计示意图。b，颜色编码簇的时空分布。c，具有生物学意义的解释簇示例。d，DKD中差异丰度簇的识别。e，C19（代谢管状损伤）的时空分布图像。f，使用CellPose的细胞分割和细胞水平元簇的定义。g，具有高C19丰度的细胞水平元簇 (MC16) 示例，与近端小管 (PTs) 中的代谢损伤相关。比例尺，50 μm。

（注意：原文文档中见下一页的标题；根据提供文本编译标题。）

附加图表和数据

• 对照和DKD的投影簇。
• 簇丰度和签名。
• 药物交互和蛋白贡献者。
• 统计分析（log2[FC]、平均强度等）。
• n = 18对照 (RCC)，n = 20 DKD（晚期）。

（注意：未包括剩余35页。）