1. A ready-to-run, clean R script that builds a family-level taxonomic dendrogram filtered by abundance (> 1%), following the same logic as Benakis et al. (Figure 1C).
2. A Methods section formatted for inclusion in a manuscript, describing the same procedure in scientific writing style.

---

1. Ready-to-Run R Script

(Taxonomic dendrogram of microbial families filtered by relative abundance > 1%)

###############################################################################
# Taxonomic dendrogram of microbial families (abundance > 1%)
# Based on the Benakis et al. Figure 1C approach
# Author: [Your Name]
# Date: [Insert Date]
###############################################################################

# Load libraries
library(phyloseq)
library(dplyr)
library(tibble)
library(igraph)
library(ggraph)

# ------------------------------
# Step 1 — Agglomerate to Family level
# ------------------------------
# Assumes an existing phyloseq object: ps_filt

ps_fam <- tax_glom(ps_filt, taxrank = "Family", NArm = TRUE)
ps_fam <- prune_taxa(taxa_sums(ps_fam) > 0, ps_fam)

# Optional: restrict to selected groups if needed
# ps_fam <- subset_samples(ps_fam, Group %in% c("pre-FMT", "9", "10", "11"))

# ------------------------------
# Step 2 — Calculate relative abundance and filter families >1%
# ------------------------------

# Transform to relative abundance
ps_fam_relab <- transform_sample_counts(ps_fam, function(x) x / sum(x))

# Compute mean relative abundance across all samples
mean_abund <- taxa_sums(ps_fam_relab) / nsamples(ps_fam_relab)

# Keep only families with >1% mean relative abundance
keep_taxa <- names(mean_abund[mean_abund > 0.01])
ps_fam_top <- prune_taxa(keep_taxa, ps_fam_relab)

# ------------------------------
# Step 3 — Build taxonomic hierarchy (Phylum → Class → Order → Family)
# ------------------------------

tax <- as.data.frame(tax_table(ps_fam_top)) %>%
  rownames_to_column("taxa_id") %>%
  select(Phylum, Class, Order, Family) %>%
  mutate(across(everything(), as.character)) %>%
  distinct()

edges <- bind_rows(
  tax %>% transmute(from = Phylum, to = Class),
  tax %>% transmute(from = Class,  to = Order),
  tax %>% transmute(from = Order,  to = Family)
) %>%
  distinct() %>%
  filter(!is.na(from), !is.na(to), from != "", to != "")

# ------------------------------
# Step 4 — Plot dendrogram-style taxonomy tree
# ------------------------------

g <- graph_from_data_frame(edges, directed = TRUE)

p <- ggraph(g, layout = "dendrogram") +
  geom_edge_diagonal(color = "grey60") +
  geom_node_point(size = 2, color = "steelblue") +
  geom_node_text(aes(label = name), hjust = -0.1, size = 3) +
  theme_void() +
  coord_flip() +
  ggtitle("Taxonomic dendrogram of microbial families (>1% abundance)")

print(p)

# ------------------------------
# Optional: collapse remaining rare families as "Other"
# ------------------------------
# This can be done before filtering or directly in the figure annotation
###############################################################################

---

2. Methods Section (for Manuscript)

---

Taxonomic Dendrogram Construction

A taxonomic dendrogram illustrating the microbial composition at the family level was generated in R (version 4.3.0) using the phyloseq and ggraph packages. The analysis followed an approach similar to that presented by Benakis et al. (2020, Cell Host \& Microbe, Figure 1C).

First, the filtered amplicon sequence variant (ASV) table was agglomerated to the Family rank using the tax_glom() function from phyloseq. The dataset was then transformed to relative abundances for each sample, and families with a mean relative abundance greater than 1% across all samples were retained for visualization. Families below this threshold were excluded and considered part of the “Other” category for interpretability.

A hierarchical taxonomy table (Phylum → Class → Order → Family) was extracted, and edges representing parent–child relationships were assembled using dplyr. The resulting taxonomic relationships were converted into a directed graph structure using igraph, and visualized as a dendrogram with ggraph (layout = "dendrogram").

This approach provides a qualitative overview of microbial family-level diversity while reducing visual noise from rare taxa, consistent with the display strategy used in the original publication.

---

Would you like me to adapt the Methods version to a more formal journal submission style (e.g., third person passive voice and APA-compliant citation format)?

---

---

---

---

Generating a Taxonomic Dendrogram of Significant Microbial Families in R (Based on Benakis et al. Figure 1C)

Concept overview

Yes—this is doable in R, and you can restrict the dendrogram to only significant microbial families. Based on the structure in the Benakis et al. paper:

• Figure 1C is a taxonomic dendrogram (Phylum → Class → Order → Family), not a clustering of samples.
• The authors filtered families by frequency > 1%, not by statistical significance.

If your PI asks for “only significantly expressed families,” you first need to perform a differential abundance test at the Family level (e.g., using ANCOM-BC2 or DESeq2) before plotting only those families or highlighting them on the tree.

---

Recommended Workflow

Step 1 — Agglomerate to Family level (phyloseq)

library(phyloseq)

ps_fam <- tax_glom(ps_filt, taxrank = "Family", NArm = TRUE)
ps_fam <- prune_taxa(taxa_sums(ps_fam) > 0, ps_fam)

# Restrict to requested sample groups
ps_fam_sub <- subset_samples(ps_fam, Group %in% c("9", "10", "11", "pre-FMT"))
sample_data(ps_fam_sub)$Group <- factor(sample_data(ps_fam_sub)$Group,
                                       levels = c("pre-FMT", "9", "10", "11"))

---

Step 2 — Differential abundance to define significant families

Option A: ANCOM-BC2 (recommended for microbiome data)

library(ANCOMBC)

an2 <- ancombc2(data = ps_fam_sub,
                formula = "Group",
                p_adj_method = "BH",
                prv_cut = 0, lib_cut = 0,
                group = "Group",
                global = FALSE)

res <- an2$res
# Inspect results: names(an2$res), matrices such as res$diff_abn and res$q_val

Option B: DESeq2 (alternative)

library(DESeq2)

dds <- phyloseq_to_deseq2(ps_fam_sub, ~ Group)
dds <- DESeq(dds)

# Example contrast: Group 9 vs 10
r_9_10 <- results(dds, contrast = c("Group", "9", "10"))
sig_9_10 <- rownames(r_9_10)[which(r_9_10$padj < 0.05)]
sig_9_10 <- sig_9_10[!is.na(sig_9_10)]

Decide what “significant” means for your plot:

• Only families significant for 9 vs 10, or
• Families significant in any pairwise comparison among (9, 10, 11, pre-FMT).

---

Step 3 — Build the taxonomic dendrogram (Phylum → Class → Order → Family)

library(dplyr)
library(tibble)
library(igraph)
library(ggraph)

# Choose your significant taxa list (example from DESeq2)
sig_taxa <- sig_9_10

# Get taxonomy for significant families
tax <- as.data.frame(tax_table(ps_fam_sub)) %>%
  rownames_to_column("taxa_id") %>%
  filter(taxa_id %in% sig_taxa) %>%
  select(Phylum, Class, Order, Family) %>%
  mutate(across(everything(), as.character)) %>%
  distinct()

# Build hierarchical edges
edges <- bind_rows(
  tax %>% transmute(from = Phylum, to = Class),
  tax %>% transmute(from = Class,  to = Order),
  tax %>% transmute(from = Order,  to = Family)
) %>%
  distinct() %>%
  filter(!is.na(from), !is.na(to), from != "", to != "")

# Build graph
g <- graph_from_data_frame(edges, directed = TRUE)

# Plot dendrogram
p <- ggraph(g, layout = "dendrogram") +
  geom_edge_diagonal() +
  geom_node_point(size = 2) +
  geom_node_text(aes(label = name), hjust = -0.1, size = 3) +
  theme_void() +
  coord_flip()

p

---

Highlighting significance in the plot

To color families by direction (e.g., enriched in Group 9 vs 10), merge the log2FoldChange results from DESeq2 with the node names and use scale_color_gradient2() or similar in ggraph to map significance.

---

Summary for your report (Q1.5 suggested answer)

Yes—this can be done in R. The template figure in the Benakis paper shows a taxonomy-based dendrogram (Phylum → Class → Order → Family). In that study, families were filtered by abundance (only > 1% frequency) rather than statistical significance.

For our dataset, we can (i) construct the full family-level taxonomy tree and (ii) optionally restrict or highlight only those families that are significantly different between groups based on differential abundance testing (using DESeq2 or ANCOM-BC2 with Benjamini–Hochberg-adjusted p-values).

If you specify which comparison defines “significant” (only 9 vs 10 or across all four groups), the exact filtering and visualization code can be tailored to more closely match Figure 1C, including the “Other” bin and legend style.

---

## =========================================================
## MicrobiotaProcess_UPDATED.R
## Uses your objects:
##   - ps_filt        : for alpha + beta diversity (NOT taxa-filtered)
##   - ps_abund_rel   : for cleaner composition plots (taxa filtered for plotting)
##
## Output:
##   - Rarefaction curves + alpha diversity plots
##   - Bray (Hellinger) distance + PCoA + PERMANOVA
##   - Composition plots (Class) + heatmaps (from ps_abund_rel)
## =========================================================

## -----------------------------
## 0) Define groups (sample IDs)  ---- EXACTLY as provided
Group1<-c("U24080201","U25020701","O23092004","U24101801","U25022101","O23102703","A24062801","O23112205","U23071901","A23112002","U24111801","O23110101","U24121801","O23120101","O24011202","O23090803","A23060602","A24030402","U25011701","O24011901","U23090801","O24011201","O24011003","O23092202","O23082301","O23091403","O23112901","O23092201","O24013103","O24021403","O24010402","O23092005","O23092203","O24010302","O23090701","O23091501","O23092701","O24022202","O23092802","O23090601","O23100401","O24022102","O23081801","O23092006","O23100503","O23090602","O24013104","O24020803","O24010301","O24010404","O23090802","O23092801","O24022801","O23100706","O23102602","O24021601","O24012401","O24021603","O24022901","O24021501","O23110902","O23102601","O23102704","O23100803","O23102701","O24021401","O24022101","O24030603","O23110901","O23110903","O23110301","O24022301","O23102502","O23111501","O23111602","O24020705","O24021502","O24022201","O23110202","O23090801")

Group2<-c("U23071701","U23052401","U23052201","U24070401","O24011801","O23092003","A24071901","A24072901","O24011102","O23121501","O23092104","O23092001","O23121301","O24020701","O23112201","O23100701","O23100801","O24020903","O24020901","O24020703","O23112204")

Group3<-c("O23100802","O24011205","O23092002","O24011207","O23092103","O23102501","O24011005","A24030401","O24011004","A23051102","U25011702","O24011204","O23121502","O23120702","O24011206","O24021404","O23092101","O24010403","O23112303","O23083001","O23082302","O24010401","O24022302","O24010501","O23112902","O23082303","O23083102","O24013101","O23100402","O24020801","O23120701","O23121304","O24021602","O24011802","O23121306","O23120103","O24020905","O24012403","O24013102","O24021503","O24020904","O23102504","O24013105","O24030601","O23100705","O24030604","O23111601","O24020103","O24030602","O23110302","O23102603","O24031304","O24021402","O24020101","O24012501","O24020804","O23100804","O23102503","O24022902","O24020704","O23110904","O24020102","O24012402","O23102702","O23102604","O23110204","O23110203","O23083101","O23092702")

Group4<-c("O23112304","A23051103","A24071701","A23080101","A24031201","A24080201","O24011105","O23091305","O23121302","O23092803")

Group5<-c("O23091303","O23112301","O24011203","A23112001","O24011001","O24011002","O23091302","O24020902","O23092102","O23091401","O23121503","O23091402","O24020702","O23091301","O23112206","O24011103","O23121305","O24011101","O23121303","O24011104","O23120104","O23100704","O23112302","O23112203","O23100703","O24020805","O24020802","O23112202","O24031302","O23111502","O23100702","O24031301","O24031305","O23082402")

NTC<-c("NTC_2","NTC_3","NTC_16","NTC_4","NTC_6","NTC_7","NTC_8","NTC_9","NTC_11","NTC_12","NTC_13","NTC_14","NTC_15","NTC_10")

PC<-c("PC_1","PC_2","PC_8","PC_4","PC_3","PC_5","PC_6","PC_7","UR009768","UR009909","PC01")

## Ensure unique IDs within each vector
Group1 <- unique(Group1); Group2 <- unique(Group2); Group3 <- unique(Group3)
Group4 <- unique(Group4); Group5 <- unique(Group5); NTC <- unique(NTC); PC <- unique(PC)

## The group order you want everywhere (plots + stats)
group_levels <- c("Group1","Group2","Group3","Group4","Group5","NTC","PC")

## -----------------------------
## 0.1) Sample subset (union of all groups)
keep_samples <- unique(c(Group1, Group2, Group3, Group4, Group5, NTC, PC))

## -----------------------------
## 0.2) Helper: assign Group as an ordered factor (membership-based)
add_group <- function(mpse_obj) {
    sn <- rownames(colData(mpse_obj))
    grp <- rep(NA_character_, length(sn))

    grp[sn %in% Group1] <- "Group1"
    grp[sn %in% Group2] <- "Group2"
    grp[sn %in% Group3] <- "Group3"
    grp[sn %in% Group4] <- "Group4"
    grp[sn %in% Group5] <- "Group5"
    grp[sn %in% NTC]    <- "NTC"
    grp[sn %in% PC]     <- "PC"

    colData(mpse_obj)$Group <- factor(grp, levels = group_levels)

    # warn if any samples weren't assigned (helps catch typos / missing IDs)
    if (any(is.na(colData(mpse_obj)$Group))) {
        warning(
            "Unassigned samples (not found in any group list): ",
            paste(sn[is.na(colData(mpse_obj)$Group)], collapse = ", ")
        )
    }
    mpse_obj
}

## -----------------------------
## 0.3) Colors (edit to taste)
group_colors <- c(
    "Group1" = "#1f77b4",
    "Group2" = "#ff7f0e",
    "Group3" = "#2ca02c",
    "Group4" = "#d62728",
    "Group5" = "#9467bd",
    "NTC"    = "#7f7f7f",
    "PC"     = "#8c564b"
)

## =========================================================
## 1) Diversity analysis object (alpha + beta)
##    IMPORTANT: start from ps_filt (all taxa retained)
## =========================================================
ps_div <- prune_samples(keep_samples, ps_filt)
ps_div <- prune_taxa(taxa_sums(ps_div) > 0, ps_div)

mpse_div <- as.MPSE(ps_div)
mpse_div <- add_group(mpse_div)

cat("\n[mpse_div] Group counts:\n")
print(table(colData(mpse_div)$Group, useNA = "ifany"))

## =========================================================
## 2) Alpha diversity (rarefaction-based)
## =========================================================
set.seed(9242)
mpse_div %<>% mp_rrarefy()  # creates RareAbundance
mpse_div %<>% mp_cal_rarecurve(.abundance = RareAbundance, chunks = 400)

# Rarefaction curves: sample + grouped
p_rare_1 <- mpse_div %>% mp_plot_rarecurve(.rare = RareAbundanceRarecurve, .alpha = Observe)

p_rare_2 <- mpse_div %>%
    mp_plot_rarecurve(.rare = RareAbundanceRarecurve, .alpha = Observe, .group = Group) +
    scale_color_manual(values = group_colors, guide = "none") +
    scale_fill_manual(values = group_colors, guide = "none")

p_rare_3 <- mpse_div %>%
    mp_plot_rarecurve(.rare = RareAbundanceRarecurve, .alpha = "Observe",
                                        .group = Group, plot.group = TRUE) +
    scale_color_manual(values = group_colors, guide = "none") +
    scale_fill_manual(values = group_colors, guide = "none")

png("figures/rarefaction_of_samples_or_groups.png", width = 1080, height = 600)
print(p_rare_1 + p_rare_2 + p_rare_3)
dev.off()

# Alpha indices from RareAbundance
mpse_div %<>% mp_cal_alpha(.abundance = RareAbundance)

f_alpha_1 <- mpse_div %>%
    mp_plot_alpha(.group = Group, .alpha = c(Observe, Chao1, ACE, Shannon, Simpson, Pielou)) +
    scale_color_manual(values = group_colors, guide = "none") +
    scale_fill_manual(values = group_colors, guide = "none")

f_alpha_2 <- mpse_div %>%
    mp_plot_alpha(.alpha = c(Observe, Chao1, ACE, Shannon, Simpson, Pielou))

png("figures/alpha_diversity_comparison.png", width = 1400, height = 600)
print(f_alpha_1 / f_alpha_2)
dev.off()

## =========================================================
## 3) Beta diversity (Bray–Curtis on Hellinger)
##    IMPORTANT: use non-rarefied Abundance (not taxa-filtered)
## =========================================================
mpse_div %<>% mp_decostand(.abundance = Abundance)             # creates 'hellinger'
mpse_div %<>% mp_cal_dist(.abundance = hellinger, distmethod = "bray")

# Distance between samples
p_dist_1 <- mpse_div %>% mp_plot_dist(.distmethod = bray)
png("figures/distance_between_samples.png", width = 1000, height = 1000)
print(p_dist_1)
dev.off()

# Distance with group info
p_dist_2 <- mpse_div %>% mp_plot_dist(.distmethod = bray, .group = Group) +
    scale_fill_manual(values = group_colors) +
    scale_color_gradient()

png("figures/distance_between_samples_with_group_info.png", width = 1000, height = 1000)
print(p_dist_2)
dev.off()

# Compare distances between groups (Bray)
p_dist_3 <- mpse_div %>%
    mp_plot_dist(.distmethod = bray, .group = Group, group.test = TRUE, textsize = 6) +
    theme(
        axis.title.x = element_text(size = 14),
        axis.title.y = element_text(size = 14),
        axis.text.x  = element_text(size = 14),
        axis.text.y  = element_text(size = 14)
    )

png("figures/Comparison_of_Bray_Distances.png", width = 1000, height = 1000)
print(p_dist_3)
dev.off()

## PCoA + PERMANOVA (adonis2)
mpse_div %<>% mp_cal_pcoa(.abundance = hellinger, distmethod = "bray")

mpse_div %<>% mp_adonis(
    .abundance   = hellinger,
    .formula     = ~ Group,
    distmethod   = "bray",
    permutations = 9999,
    action       = "add"
)

cat("\n[PERMANOVA result]\n")
print(mpse_div %>% mp_extract_internal_attr(name = "adonis"))

## PCoA plot
p_pcoa_1 <- mpse_div %>%
    mp_plot_ord(
        .ord   = pcoa,
        .group = Group,
        .color = Group,
        .size  = 4,
        .alpha = 1,
        ellipse = TRUE,
        show.legend = TRUE
    ) +
    scale_fill_manual(values = group_colors) +
    scale_color_manual(values = group_colors) +
    theme(
        axis.text   = element_text(size = 16),
        axis.title  = element_text(size = 18),
        legend.text = element_text(size = 14),
        legend.title= element_text(size = 16)
    )

png("figures/PCoA.png", width = 1200, height = 1000)
print(p_pcoa_1)
dev.off()

pdf("figures/PCoA.pdf", width = 10, height = 8)
print(p_pcoa_1)
dev.off()

## Optional: label points
colData(mpse_div)$ShortLabel <- gsub("sample-", "", rownames(colData(mpse_div)))

p_pcoa_2 <- p_pcoa_1 +
    geom_text_repel(aes(label = ShortLabel), size = 4, max.overlaps = 100)

png("figures/PCoA_labeled.png", width = 1200, height = 1000)
print(p_pcoa_2)
dev.off()
#[PERMANOVA result]
#The object contained internal attribute: PCoA ADONIS
#Permutation test for adonis under reduced model
#Permutation: free
#Number of permutations: 9999
#
#vegan::adonis2(formula = .formula, data = sampleda, permutations = permutations, method = distmethod)
#          Df SumOfSqs      R2      F Pr(>F)
#Model      6   11.446 0.16058 7.3971  1e-04 ***
#Residual 232   59.829 0.83942
#Total    238   71.274 1.00000
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

## =========================================================
## 4) Composition plots object (clean taxa set for plotting)
##    IMPORTANT: start from ps_abund_rel (plotting-filtered taxa)
## =========================================================
ps_plot <- prune_samples(keep_samples, ps_abund_rel)
ps_plot <- prune_taxa(taxa_sums(ps_plot) > 0, ps_plot)

mpse_plot <- as.MPSE(ps_plot)
mpse_plot <- add_group(mpse_plot)

## Summaries for plotting (Class)
mpse_plot %<>%
    mp_cal_abundance(.abundance = Abundance) %>%               # per sample
    mp_cal_abundance(.abundance = Abundance, .group = Group)   # per group

## Class abundance barplots (top 20)
p_class_rel <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        taxa.class = Class,
        topn = 20,
        relative = TRUE
    )

p_class_abs <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        taxa.class = Class,
        topn = 20,
        relative = FALSE
    )

png("figures/relative_abundance_and_abundance_samples.png", width = 1200, height = 600)
print(p_class_rel / p_class_abs)
dev.off()

## Heatmaps (grouped)
h_rel <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        .group = Group,
        taxa.class = Class,
        relative = TRUE,
        topn = 20,
        geom = "heatmap",
        features.dist = "euclidean",
        features.hclust = "average",
        sample.dist = "bray",
        sample.hclust = "average"
    )

h_abs <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        .group = Group,
        taxa.class = Class,
        relative = FALSE,
        topn = 20,
        geom = "heatmap",
        features.dist = "euclidean",
        features.hclust = "average",
        sample.dist = "bray",
        sample.hclust = "average"
    )

png("figures/relative_abundance_and_abundance_heatmap.png", width = 1200, height = 600)
print(aplot::plot_list(gglist = list(h_rel, h_abs), tag_levels = "A"))
dev.off()

## Group-level barplots
p_group_rel <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        .group = Group,
        taxa.class = Class,
        topn = 20,
        plot.group = TRUE,
        relative = TRUE
    ) +
    scale_fill_manual(values = group_colors)

p_group_abs <- mpse_plot %>%
    mp_plot_abundance(
        .abundance = Abundance,
        .group = Group,
        taxa.class = Class,
        topn = 20,
        plot.group = TRUE,
        relative = FALSE
    ) +
    scale_fill_manual(values = group_colors)

png("figures/relative_abundance_and_abundance_groups.png", width = 1000, height = 1000)
print(p_group_rel / p_group_abs)
dev.off()

cat("\nDONE. Outputs written to ./figures/\n")

## =========================================================
## CONTINUE: Export Bray distances + pairwise PERMANOVA
## (Use mpse_div from the updated script above)
## =========================================================

suppressPackageStartupMessages({
    library(dplyr)
    library(tidyr)
    library(openxlsx)
    library(vegan)
})

## -----------------------------
## Helper: get assay matrix with rows = samples, cols = features
get_sample_by_feature <- function(mpse_obj, assay_name) {
    mat <- assay(mpse_obj, assay_name)

    # sample IDs in MPSE
    samp_ids <- rownames(colData(mpse_obj))

    # If samples are columns, transpose
    if (!is.null(colnames(mat)) && all(samp_ids %in% colnames(mat))) {
        mat <- t(mat)
    }

    # Now enforce row order to match colData
    mat <- mat[samp_ids, , drop = FALSE]
    mat
}

## -----------------------------
## 1) Recompute Bray–Curtis distance (robust extraction)
hell_mat_sf <- get_sample_by_feature(mpse_div, "hellinger")  # rows=samples, cols=features
bray_dist   <- vegdist(hell_mat_sf, method = "bray")

## sanity checks
stopifnot(!any(is.na(as.matrix(bray_dist))))
stopifnot(!any(as.matrix(bray_dist) < 0, na.rm = TRUE))

## -----------------------------
## 2) Export all pairwise Bray distances to Excel
bray_mat <- as.matrix(bray_dist)
samples  <- rownames(bray_mat)

bray_df <- as.data.frame(as.table(bray_mat)) %>%
    rename(Sample1 = Var1, Sample2 = Var2, BrayDistance = Freq) %>%
    filter(Sample1 < Sample2) %>%
    arrange(Sample1, Sample2)

write.xlsx(bray_df, file = "figures/Bray_Curtis_Distances.xlsx")

## -----------------------------
## 3) Pairwise PERMANOVA (post-hoc) using vegan::adonis2
meta2 <- as.data.frame(colData(mpse_div))
meta2 <- meta2[rownames(hell_mat_sf), , drop = FALSE]
meta2$Group <- droplevels(meta2$Group)

groups <- levels(meta2$Group)

res_list <- list()
k <- 1

for (i in 1:(length(groups) - 1)) {
    for (j in (i + 1):length(groups)) {

        g1 <- groups[i]
        g2 <- groups[j]

        idx <- meta2$Group %in% c(g1, g2)
        sub_meta <- meta2[idx, , drop = FALSE]

        sub_dist <- as.dist(as.matrix(bray_dist)[idx, idx])

        ad <- adonis2(sub_dist ~ Group, data = sub_meta, permutations = 9999)

        res_list[[k]] <- data.frame(
            group1 = g1,
            group2 = g2,
            F      = ad$F[1],
            R2     = ad$R2[1],
            p      = ad$`Pr(>F)`[1]
        )
        k <- k + 1
    }
}

pair_res <- do.call(rbind, res_list)
pair_res$p_adj_BH         <- p.adjust(pair_res$p, method = "BH")
pair_res$p_adj_Bonferroni <- p.adjust(pair_res$p, method = "bonferroni")

write.csv(pair_res, "figures/Bray_pairwise_PERMANOVA.csv", row.names = FALSE)

cat("\nPairwise PERMANOVA written to: figures/Bray_pairwise_PERMANOVA.csv\n")
cat("Bray distance table written to: figures/Bray_Curtis_Distances.xlsx\n")

## =========================================================
## OPTIONAL: PCoA plot where point size = Shannon and alpha = Observe
## (requires mpse_div already has Shannon/Observe from mp_cal_alpha)
## =========================================================

p_pcoa_sizealpha <- mpse_div %>%
    mp_plot_ord(
        .ord   = pcoa,
        .group = Group,
        .color = Group,
        .size  = Shannon,
        .alpha = Observe,
        ellipse = TRUE,
        show.legend = TRUE
    ) +
    scale_fill_manual(values = group_colors) +
    scale_color_manual(values = group_colors) +
    scale_size_continuous(range = c(2, 6)) +
    theme(
        axis.text   = element_text(size = 16),
        axis.title  = element_text(size = 18),
        legend.text = element_text(size = 14),
        legend.title= element_text(size = 16)
    )

png("figures/PCoA_sizealpha.png", width = 1200, height = 1000)
print(p_pcoa_sizealpha)
dev.off()

pdf("figures/PCoA_sizealpha.pdf", width = 10, height = 8)
print(p_pcoa_sizealpha)
dev.off()

## =========================================================
## Ensure all three ordination outputs exist:
##   - PCoA  : basic (color/group)
##   - PCoA2 : size = Shannon, alpha = Observe
##   - PCoA3 : same as PCoA2 + sample labels
##
## Assumes you already have:
##   - mpse_div with: pcoa, Group, Shannon, Observe
##   - group_colors defined
## =========================================================

p1 <- mpse_div %>%
    mp_plot_ord(
        .ord   = pcoa,
        .group = Group,
        .color = Group,
        .size  = 4,
        .alpha = 1,
        ellipse = TRUE,
        show.legend = FALSE
    ) +
    scale_fill_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 1.6,
            keyheight = 1.6,
            label.theme = element_text(size = 16)
        )
    ) +
    scale_color_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 1.6,
            keyheight = 1.6,
            label.theme = element_text(size = 16)
        )
    ) +
    theme(
        axis.text   = element_text(size = 20),
        axis.title  = element_text(size = 22),
        legend.text = element_text(size = 20),
        legend.title= element_text(size = 22),
        plot.title  = element_text(size = 24, face = "bold"),
        plot.subtitle = element_text(size = 20)
    )

png("PCoA.png", width = 1200, height = 1000)
p1
dev.off()
pdf("PCoA.pdf")
p1
dev.off()

p2 <- mpse_div %>%
    mp_plot_ord(
        .ord   = pcoa,
        .group = Group,
        .color = Group,
        .size  = Shannon,
        .alpha = Observe,
        ellipse = TRUE,
        show.legend = FALSE
    ) +
    scale_fill_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    scale_color_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    scale_size_continuous(
        range = c(2, 6),
        guide = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    theme(
        axis.text   = element_text(size = 20),
        axis.title  = element_text(size = 22),
        legend.text = element_text(size = 20),
        legend.title= element_text(size = 22),
        plot.title  = element_text(size = 24, face = "bold"),
        plot.subtitle = element_text(size = 20)
    )

png("PCoA2.png", width = 1200, height = 1000)
p2
dev.off()
pdf("PCoA2.pdf")
p2
dev.off()

library(ggrepel)
colData(mpse_div)$ShortLabel <- gsub("sample-", "", mpse_div@colData@rownames)

p3 <- mpse_div %>%
    mp_plot_ord(
        .ord   = pcoa,
        .group = Group,
        .color = Group,
        .size  = Shannon,
        .alpha = Observe,
        ellipse = TRUE,
        show.legend = FALSE
    ) +
    geom_text_repel(aes(label = ShortLabel), size = 5, max.overlaps = 100) +
    scale_fill_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    scale_color_manual(
        values = group_colors,
        guide  = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    scale_size_continuous(
        range = c(2, 6),
        guide = guide_legend(
            keywidth = 0.6,
            keyheight = 0.6,
            label.theme = element_text(size = 16)
        )
    ) +
    theme(
        axis.text   = element_text(size = 20),
        axis.title  = element_text(size = 22),
        legend.text = element_text(size = 20),
        legend.title= element_text(size = 22),
        plot.title  = element_text(size = 24, face = "bold"),
        plot.subtitle = element_text(size = 20)
    )

png("PCoA3.png", width = 1200, height = 1000)
p3
dev.off()
svg("PCoA3.svg", width = 12, height = 10)
p3
dev.off()
pdf("PCoA3.pdf")
p3
dev.off()

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 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
#rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
#options(max.print = 1e6)
```

# 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

## Create analysis-specific phyloseq objects

We maintain **one filtered “base” phyloseq object** and then derive **analysis-specific** objects from it.
This avoids accidental overwriting and ensures each analysis uses the appropriate data scale (counts vs. relative abundance vs. rarefied counts).

- **`ps_raw`** → Raw imported phyloseq object (**integer counts**; import stage only)
- **`ps_base`** → `ps_raw` with **taxonomy + sample metadata** properly aligned (the clean master object before any filtering)
- **`ps_pruned`** → Optional **sample subset** of `ps_base` (e.g., drop unwanted samples by ID/pattern); still **integer counts**
- **`ps_filt`** → The shared filtered backbone: **low-depth samples removed** + taxa with **zero total counts dropped**; remains **integer counts**

```{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_raw <- qza_to_phyloseq(
            features =  "dada2_tests/test_59_f235_r245/table.qza",
            tree = "rooted-tree.qza",
            metadata = "qiime2_metadata_for_qza_to_phyloseq.tsv"
        )

        # Refresh/ensure sample_data (optional but keeps things explicit)
        sample_df <- read.csv("./qiime2_metadata_for_qza_to_phyloseq.tsv", sep="\t", row.names=1, check.names=FALSE)
        SAM <- sample_data(sample_df, errorIfNULL = TRUE)

        # Add taxonomy table (exported from QIIME2)
        taxonomy <- read.delim("./exported-taxonomy/taxonomy.tsv", sep="\t", header=TRUE)

        # 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))

        # Base object: raw integer counts + metadata + taxonomy
        ps_base <- merge_phyloseq(ps_raw, SAM, tax_table_final)
        print(ps_base)

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

Visualize data
```{r, echo=TRUE, warning=FALSE}
# Inspect the base object (raw integer counts)
sample_names(ps_base)
rank_names(ps_base)
sample_variables(ps_base)

# Optional: prune to a naming pattern (avoids hard-coding long sample lists)
#samples_keep <- sample_names(ps_base)
#samples_keep <- samples_keep[grepl("^sample-[A-H]", samples_keep)]
samples_keep <- c("O23092004","O24010402","O23083101","A23080101","PC_8","O23092702","O24010401","O23092001","O23082402","U25011701","O23120702","O23121301","O23112304","U24111801","PC01","A24080201","A23060602","A23051102","UR009768","O24031305","O23090801","UR009909","U24121801","O23120103","U23090801","O23121501","O23110901","A23051103","O24013102","O24011801","O23091403","O23102704","O24020903","A24030402","O23100401","U24101801","O24011105","O24010302","O23121502","O23092005","O24021402","O23102602","NTC_3","O23111502","PC_7","A24071701","O23111501","O24011005","A23112001","U24080201","O24010404","O23112205","O23092803","NTC_6","NTC_8","O24011001","O24010301","O24013104","O24020103","O24011201","O23102601","NTC_2","A23112002","O23102502","O24020901","O24021502","O24021503","O23112901","O23112303","O23112201","O23112902","O24020703","O24010403","O23092003","U23052401","O24021501","O23120701","NTC_11","O24022101","O24011202","O23110902","O23092701","O24011002","A24031201","U23052201","O24021401","O23091303","O24021601","NTC_13","O23092104","O24011102","O23092102","NTC_10","O24022302","A24072901","O24022301","O24022202","O24022901","A24062801","O24011004","O23090602","O23112204","O23100701","O24012403","O24011003","A24071901","O24020802","O24021403","O24021602","NTC_14","NTC_9","PC_1","O24011901","O24013103","O24021404","O24030601","O24020701","O24011204","O24011103","A24030401","NTC_4","O24020902","O23092201","NTC_7","U23071901","O24020904","O23110903","U25011702","U23071701","O24031304","O24030602","O23091501","O24012401","O24030603","O24020704","PC_4","O23110904","O24020804","O23100801","O24030604","O23100802","NTC_15","O24022201","O24012501","O23100706","O23090601","O23100803","O24031301","O23082303","O24010501","O24022902","O23102703","O23110203","O23092202","NTC_12","O23102701","O23082302","O23121304","NTC_16","O24020803","O23090802","O24020101","U25020701","O23121306","O23082301","O23092203","O23110302","O23120101","O24020905","PC_5","O23092802","U25022101","O23100702","PC_6","O23110301","O23081801","O24020705","O24011207","O23091302","O23083001","O23102503","O23121303","O24011206","O24011802","O23102504","O23110204","O24022102","O23120104","O23092002","O23102603","O23092101","O23100503","O23092103","O23111601","O24012402","O24013105","PC_3","O23092801","O23091301","O23100402","O23121503","O24011104","O23112206","O23100703","O23102702","O24022801","O24011203","O23091401","O23090803","O23100705","O23102604","O23091402","PC_2","O23121302","U24070401","O23092006","O23100704","O23112203","O24020702","O24020102","O23112301","O23100804","O24020805","O23121305","O23112202","O24011205","O24013101","O24020801","O23110202","O24031302","O23111602","O23102501","O23110101","O23090701","O24011101","O23091305","O23083102","O24021603","O23112302")
ps_pruned <- prune_samples(samples_keep, ps_base)

# Drop taxa absent after pruning
ps_pruned <- prune_taxa(taxa_sums(ps_pruned) > 0, ps_pruned)

# Quick sanity checks
nsamples(ps_base); ntaxa(ps_base)
nsamples(ps_pruned); ntaxa(ps_pruned)
```

Preprocessing statistics for each sample
```{r, echo=TRUE, warning=FALSE}
 denoising_stats <- read.csv("dada2_tests/test_59_f235_r245/data_stats.tsv", sep="\t")
 # Display the table
 kable(denoising_stats, caption = "Preprocessing statistics for each sample") %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

In the QC filtering step, we removed 14 samples that either fell below the minimum sequencing depth (library size < 12,201 reads) or were pre-specified for exclusion via sample_ids (as defined in the previous step). The filtered object (ps_filt) therefore contains only samples meeting the depth cutoff, and taxa were re-pruned to retain only those with nonzero total abundance across the retained samples.

```{r, echo=TRUE, warning=FALSE}
# ------------------------------------------------------------
#   Filter low-depth samples (recommended for all analyses)
# ------------------------------------------------------------
min_depth <- 12201  # <-- adjust to your data / study design, keeps all!
ps_filt <- prune_samples(sample_sums(ps_pruned) >= min_depth, ps_pruned)
ps_filt <- prune_taxa(taxa_sums(ps_filt) > 0, ps_filt)
ps_filt

# Keep a depth summary for reporting / QC
depth_summary <- summary(sample_sums(ps_filt))
depth_summary
```

**Differential abundance (DESeq2)** → **`ps_deseq`**: **non-rarefied integer counts** derived from `ps_filt`, with optional **count-based** taxon prefilter
    *(default: taxa total counts ≥ 10 across all samples)*

From `ps_filt` (e.g. 5669 taxa and 239 samples), we branch into analysis-ready objects in two directions:

* Direction 1 for diversity analyses

    - **Alpha diversity**: `ps_rarefied` ✅ (common)
    - **Beta diversity**:
        - **Unweighted UniFrac / Jaccard**: `ps_rarefied` ✅ (often recommended)
        - **Bray–Curtis / ordination on abundances**: `ps_rel` or Hellinger ✅ (rarefaction optional)
        - **Aitchison (CLR)**: CLR-transformed (non-rarefied) ✅ (no rarefaction)

Rarefaction

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

# RAREFACTION
set.seed(9242)  # This will help in reproducing the filtering and nomalisation.
ps_rarefied <- rarefy_even_depth(ps_filt, sample.size = 12201)

# # 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_rel <- microbiome::transform(ps.ng.tax, "compositional")
#
# saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
```

* Direction 2 for taxonomic composition plots

    - **Taxonomic composition** → **`ps_rel`**: **relative abundance** (compositional) computed **after sample filtering** (e.g. 5669 taxa and 239 samples)
    - **Optional cleaner composition plots** → **`ps_abund` / `ps_abund_rel`**: taxa filtered for plotting (e.g., keep taxa with **mean relative abundance > 0.1%**); (e.g. 95 taxa and 239 samples)
    `ps_abund` = **counts**, `ps_abund_rel` = **relative abundance** *(use for visualization, not DESeq2)*

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}
# 1) Convert to relative abundances
ps_rel <- transform_sample_counts(ps_filt, 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_rel,
    function(x) mean(x) > 0.001,
    prune = FALSE
)

# 3) Use the TRUE/FALSE vector to subset absolute abundance data
ps_abund <- prune_taxa(names(keep_vector)[keep_vector], ps_filt)

# 4) Normalize the final subset to relative abundances per sample
ps_abund_rel <- transform_sample_counts(
    ps_abund,
    function(x) x / sum(x)
)
```

# Heatmaps

```{r, echo=FALSE, warning=FALSE}
datamat_ = as.data.frame(otu_table(ps_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("O23092004","O24010402","O23083101","A23080101","PC_8","O23092702","O24010401","O23092001","O23082402","U25011701","O23120702","O23121301","O23112304","U24111801","PC01","A24080201","A23060602","A23051102","UR009768","O24031305","O23090801","UR009909","U24121801","O23120103","U23090801","O23121501","O23110901","A23051103","O24013102","O24011801","O23091403","O23102704","O24020903","A24030402","O23100401","U24101801","O24011105","O24010302","O23121502","O23092005","O24021402","O23102602","NTC_3","O23111502","PC_7","A24071701","O23111501","O24011005","A23112001","U24080201","O24010404","O23112205","O23092803","NTC_6","NTC_8","O24011001","O24010301","O24013104","O24020103","O24011201","O23102601","NTC_2","A23112002","O23102502","O24020901","O24021502","O24021503","O23112901","O23112303","O23112201","O23112902","O24020703","O24010403","O23092003","U23052401","O24021501","O23120701","NTC_11","O24022101","O24011202","O23110902","O23092701","O24011002","A24031201","U23052201","O24021401","O23091303","O24021601","NTC_13","O23092104","O24011102","O23092102","NTC_10","O24022302","A24072901","O24022301","O24022202","O24022901","A24062801","O24011004","O23090602","O23112204","O23100701","O24012403","O24011003","A24071901","O24020802","O24021403","O24021602","NTC_14","NTC_9","PC_1","O24011901","O24013103","O24021404","O24030601","O24020701","O24011204","O24011103","A24030401","NTC_4","O24020902","O23092201","NTC_7","U23071901","O24020904","O23110903","U25011702","U23071701","O24031304","O24030602","O23091501","O24012401","O24030603","O24020704","PC_4","O23110904","O24020804","O23100801","O24030604","O23100802","NTC_15","O24022201","O24012501","O23100706","O23090601","O23100803","O24031301","O23082303","O24010501","O24022902","O23102703","O23110203","O23092202","NTC_12","O23102701","O23082302","O23121304","NTC_16","O24020803","O23090802","O24020101","U25020701","O23121306","O23082301","O23092203","O23110302","O23120101","O24020905","PC_5","O23092802","U25022101","O23100702","PC_6","O23110301","O23081801","O24020705","O24011207","O23091302","O23083001","O23102503","O23121303","O24011206","O24011802","O23102504","O23110204","O24022102","O23120104","O23092002","O23102603","O23092101","O23100503","O23092103","O23111601","O24012402","O24013105","PC_3","O23092801","O23091301","O23100402","O23121503","O24011104","O23112206","O23100703","O23102702","O24022801","O24011203","O23091401","O23090803","O23100705","O23102604","O23091402","PC_2","O23121302","U24070401","O23092006","O23100704","O23112203","O24020702","O24020102","O23112301","O23100804","O24020805","O23121305","O23112202","O24011205","O24013101","O24020801","O23110202","O24031302","O23111602","O23102501","O23110101","O23090701","O24011101","O23091305","O23083102","O24021603","O23112302")]
# Remove rows with zero variance
datamat <- datamat[apply(datamat, 1, var) > 0, ]
# Remove cols with zero variance
#datamat <- datamat[, apply(datamat, 2, var) > 0]

# (optional) replace with your actual column names
#colnames(datamat) <- c(
#    "A24040201",...
#  )

# ---------- 0) Sample names from datamat ----------
samplenames <- sub("^sample-", "", colnames(datamat))

# ---------- 1) Read metadata ----------
meta_path <- "qiime2_metadata.tsv"
meta <- read.delim(
    meta_path,
    sep = "\t",
    header = TRUE,
    stringsAsFactors = FALSE,
    check.names = FALSE,
    comment.char = ""
)

# ---------- 2) Identify SampleID + Group columns ----------
sample_id_col <- c("#SampleID","SampleID","sample-id","sampleid")
sample_id_col <- sample_id_col[sample_id_col %in% colnames(meta)][1]
if (is.na(sample_id_col)) sample_id_col <- colnames(meta)[1]

group_col <- c("Group","group","GROUP")
group_col <- group_col[group_col %in% colnames(meta)][1]
if (is.na(group_col)) stop("No 'Group' column found in metadata.")

# ---------- 3) Build lookup: sample -> group ----------
meta_ids <- sub("^sample-", "", meta[[sample_id_col]])
meta_groups <- trimws(as.character(meta[[group_col]]))
group_map <- setNames(meta_groups, meta_ids)

# Map datamat columns to group labels
groups <- unname(group_map[samplenames])
groups[is.na(groups)] <- "unknown"

# ---------- 4) Color mapping for YOUR labels ----------
# (Adjust colors if you prefer different ones)
color_map <- c(
    "1" = "#a6cee3",
    "2" = "#1f78b4",
    "3" = "#b2df8a",
    "4" = "#33a02c",
    "5" = "#fb9a99",
    "negative control" = "#6a3d9a",
    "positive control" = "#ff7f00",
    "unknown" = "GREY"
)

# Assign colors safely
sampleCols <- unname(color_map[groups])
sampleCols[is.na(sampleCols)] <- "GREY"
names(sampleCols) <- samplenames

# ---------- 5) Checks ----------
cat("Unique groups found in datamat:\n")
print(sort(unique(groups)))
cat("\nCounts per group:\n")
print(table(groups, useNA = "ifany"))
cat("\nFirst 10 sample colors:\n")
print(head(sampleCols, 10))

# Optional: list any samples that didn't match metadata
unmatched <- samplenames[groups == "unknown"]
if (length(unmatched) > 0) {
    cat("\nWARNING: Unmatched samples (showing up to 20):\n")
    print(head(unmatched, 20))
}
```

```{r, echo=TRUE, warning=FALSE, fig.cap="Heatmap", out.width = '100%', fig.align= "center"}
## --- 1) order columns by group (and then by sample name) ---
group_order <- c("1","2","3","4","5","negative control","positive control","unknown")
groups_fac  <- factor(groups, levels = group_order, ordered = TRUE)

col_order <- order(groups_fac, samplenames)

datamat_ord     <- datamat[, col_order, drop = FALSE]
groups_ord      <- groups[col_order]
samplenames_ord <- samplenames[col_order]
sampleCols_ord  <- sampleCols[col_order]

stopifnot(identical(colnames(datamat_ord), samplenames_ord))

## group separators
grp_counts <- table(factor(groups_ord, levels = group_order))
grp_breaks <- cumsum(as.integer(grp_counts[grp_counts > 0]))

## --- 2) cluster ROWS using the *ordered* matrix (columns don't matter, but be consistent) ---
hr   <- hclust(as.dist(1 - cor(t(datamat_ord), method = "pearson")), method = "complete")
mycl <- cutree(hr, h = max(hr$height) / 1.08)

mycol_palette <- c("YELLOW","DARKBLUE","DARKORANGE","DARKMAGENTA","DARKCYAN","DARKRED",
                                     "MAROON","DARKGREEN","LIGHTBLUE","PINK","MAGENTA","LIGHTCYAN",
                                     "LIGHTGREEN","BLUE","ORANGE","CYAN","RED","GREEN")
mycol <- mycol_palette[as.vector(mycl)]

## --- 3) plot using datamat_ord and sampleCols_ord; keep column order fixed ---
library(RColorBrewer)
heatmap_colors <- colorRampPalette(brewer.pal(9, "Blues"))(100)

png("figures/heatmap.png", width = 1800, height = 2400)
heatmap.2(
    as.matrix(datamat_ord),
    Rowv = as.dendrogram(hr),
    Colv = NA,                        # IMPORTANT: do NOT cluster columns
    dendrogram = "row",
    scale = "row",
    trace = "none",
    col = heatmap_colors,
    cexRow = 1.2,
    cexCol = 0.8,
    RowSideColors = mycol,
    ColSideColors = sampleCols_ord,   # IMPORTANT: use ordered colors
    srtCol = 85,
    labRow = row.names(datamat_ord),
    labCol = samplenames_ord,         # optional but explicit
    key = TRUE,
    margins = c(10, 15),
    lhei = c(0.7, 15),
    lwid = c(1, 8),
    colsep = grp_breaks,              # optional separators
    sepcolor = "black",
    sepwidth = c(0.02, 0.02)
)
dev.off()

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

\pagebreak

```{r, echo=FALSE, warning=FALSE}
    library(stringr)
#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_abund_rel)[${id},\"Domain\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Domain\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Phylum\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Phylum\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Class\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Class\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Order\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Order\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Family\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Family\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Genus\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Genus\"], \"__\")[[1]][2]"
#  echo "phyloseq::tax_table(ps_abund_rel)[${id},\"Species\"] <- str_split(phyloseq::tax_table(ps_abund_rel)[${id},\"Species\"], \"__\")[[1]][2]"
#done

phyloseq::tax_table(ps_abund_rel)[1,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[1,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[1,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[2,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[2,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[3,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[3,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[4,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[4,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[5,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[5,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[6,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[6,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[7,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[7,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[8,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[8,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[9,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[9,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[10,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[10,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[11,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[11,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[12,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[12,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[13,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[13,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[14,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[14,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[15,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[15,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[16,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[16,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[17,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[17,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[18,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[18,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[19,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[19,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[20,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[20,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[21,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[21,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[22,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[22,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[23,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[23,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[24,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[24,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[25,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[25,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[26,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[26,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[27,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[27,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[28,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[28,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[29,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[29,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[30,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[30,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[31,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[31,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[32,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[32,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[33,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[33,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[34,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[34,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[35,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[35,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[36,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[36,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[37,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[37,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[38,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[38,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[39,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[39,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[40,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[40,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[41,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[41,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[42,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[42,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[43,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[43,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[44,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[44,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[45,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[45,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[46,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[46,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[47,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[47,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[48,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[48,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[49,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[49,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[50,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[50,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[51,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[51,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[52,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[52,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[53,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[53,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[54,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[54,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[55,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[55,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[56,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[56,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[57,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[57,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[58,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[58,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[59,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[59,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[60,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[60,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[61,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[61,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[62,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[62,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[63,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[63,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[64,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[64,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[65,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[65,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[66,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[66,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[67,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[67,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[68,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[68,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[69,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[69,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[70,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[70,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[71,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[71,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[72,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[72,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[73,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[73,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[74,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[74,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[75,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[75,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[76,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[76,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[77,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[77,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[78,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[78,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[79,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[79,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[80,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[80,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[81,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[81,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[82,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[82,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[83,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[83,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[84,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[84,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[85,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[85,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[86,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[86,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[87,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[87,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[88,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[88,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[89,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[89,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[90,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[90,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[91,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[91,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[92,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[92,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[93,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[93,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[94,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[94,"Species"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Domain"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Domain"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Phylum"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Phylum"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Class"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Class"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Order"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Order"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Family"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Family"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Genus"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Genus"], "__")[[1]][2]
phyloseq::tax_table(ps_abund_rel)[95,"Species"] <- str_split(phyloseq::tax_table(ps_abund_rel)[95,"Species"], "__")[[1]][2]
```

# Taxonomic summary

## Bar plots in phylum level

```{r, fig.width=16, fig.height=8, echo=TRUE, warning=FALSE}
    sample_order <- c(
            "U24080201","U25020701","O23092004","U24101801","U25022101","O23102703","A24062801","O23112205","U23071901","A23112002","U24111801","O23110101","U24121801","O23120101","O24011202","O23090803","A23060602","A24030402","U25011701","O24011901","U23090801","O24011201","O24011003","O23092202","O23082301","O23091403","O23112901","O23092201","O24013103","O24021403","O24010402","O23092005","O23092203","O24010302","O23090701","O23091501","O23092701","O24022202","O23092802","O23090601","O23100401","O24022102","O23081801","O23092006","O23100503","O23090602","O24013104","O24020803","O24010301","O24010404","O23090802","O23092801","O24022801","O23100706","O23102602","O24021601","O24012401","O24021603","O24022901","O24021501","O23110902","O23102601","O23102704","O23100803","O23102701","O24021401","O24022101","O24030603","O23110901","O23110903","O23110301","O24022301","O23102502","O23111501","O23111602","O24020705","O24021502","O24022201","O23110202","O23090801","U23071701","U23052401","U23052201","U24070401","O24011801","O23092003","A24071901","A24072901","O24011102","O23121501","O23092104","O23092001","O23121301","O24020701","O23112201","O23100701","O23100801","O24020903","O24020901","O24020703","O23112204","O23100802","O24011205","O23092002","O24011207","O23092103","O23102501","O24011005","A24030401","O24011004","A23051102","U25011702","O24011204","O23121502","O23120702","O24011206","O24021404","O23092101","O24010403","O23112303","O23083001","O23082302","O24010401","O24022302","O24010501","O23112902","O23082303","O23083102","O24013101","O23100402","O24020801","O23120701","O23121304","O24021602","O24011802","O23121306","O23120103","O24020905","O24012403","O24013102","O24021503","O24020904","O23102504","O24013105","O24030601","O23100705","O24030604","O23111601","O24020103","O24030602","O23110302","O23102603","O24031304","O24021402","O24020101","O24012501","O24020804","O23100804","O23102503","O24022902","O24020704","O23110904","O24020102","O24012402","O23102702","O23102604","O23110204","O23110203","O23083101","O23092702","O23112304","A23051103","A24071701","A23080101","A24031201","A24080201","O24011105","O23091305","O23121302","O23092803","O23091303","O23112301","O24011203","A23112001","O24011001","O24011002","O23091302","O24020902","O23092102","O23091401","O23121503","O23091402","O24020702","O23091301","O23112206","O24011103","O23121305","O24011101","O23121303","O24011104","O23120104","O23100704","O23112302","O23112203","O23100703","O24020805","O24020802","O23112202","O24031302","O23111502","O23100702","O24031301","O24031305","O23082402","NTC_2","NTC_3","NTC_16","NTC_4","NTC_6","NTC_7","NTC_8","NTC_9","NTC_11","NTC_12","NTC_13","NTC_14","NTC_15","NTC_10","UR009768","UR009909","PC01","PC_1","PC_2","PC_8","PC_4","PC_3","PC_5","PC_6","PC_7"
    )

    # create a sample ID column in sample_data (or overwrite an existing one)
    sample_data(ps_abund_rel)$SampleID <- sample_names(ps_abund_rel)

    # set the order as a factor with the desired levels
    sample_data(ps_abund_rel)$SampleID <- factor(
        sample_data(ps_abund_rel)$SampleID,
        levels = sample_order
    )

    #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_abund_rel, x = "SampleID", fill = "Phylum") +
        geom_bar(stat = "identity", position = "stack") +
        scale_fill_manual(values = my_colors) +
        theme(
            axis.text.x = element_text(
                angle = 85,           # 85° rotation
                hjust = 1,            # right-justified so labels are neat
                vjust = 1,
                size  = 5,
                colour = "black"
            ),
            axis.text.y = element_text(size = 7, colour = "black"),
            legend.position = "bottom"
        ) +
        guides(fill = guide_legend(nrow = 2))
```

### Aggregate samples by group and normalize read counts within each group to correct for differences in sequencing depth.

```{r, echo=TRUE, warning=FALSE}
    # merge + normalize
    ps_abund_rel_group  <- merge_samples(ps_abund_rel, "Group")
    ps_abund_rel_group_ <- transform_sample_counts(
        ps_abund_rel_group,
        function(x) x / sum(x)
    )

    # desired order on x-axis
    group_order <- c("1", "2", "3", "4", "5", "negative control", "positive control")

    plot_bar(ps_abund_rel_group_, fill = "Phylum") +
        geom_bar(stat = "identity", position = "stack") +
        scale_x_discrete(limits = group_order) +          # <-- set order here
        scale_fill_manual(values = my_colors) +
        labs(x = "Group") +   # <- change x-axis label from "Sample" to "Group"
        theme(axis.text = element_text(angle = 0, size = 10, colour="black"), axis.text.x = element_text(angle = -15),hjust = 10,vjust = 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_abund_rel, x = "SampleID", fill = "Class") +
        geom_bar(stat = "identity", position = "stack") +
        scale_fill_manual(values = my_colors) +
        theme(
            axis.text.x = element_text(
                angle = 85,
                hjust = 1,
                vjust = 1,
                size  = 5,
                colour = "black"
            ),
            axis.text.y = element_text(size = 7, colour = "black"),
            legend.position = "bottom"
        ) +
        guides(fill = guide_legend(nrow = 3))
```

### Aggregate samples by group and normalize read counts within each group to correct for differences in sequencing depth.

```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps_abund_rel_group_, fill="Class") + geom_bar(aes(), stat="identity", position="stack") + scale_x_discrete(limits = group_order) +
    scale_fill_manual(values = my_colors) + labs(x = "Group") + theme(axis.text = element_text(angle = 0, size = 10, colour="black"), axis.text.x = element_text(angle = -15),hjust = 10,vjust = 2)
```
\pagebreak

## 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_abund_rel, x = "SampleID", fill = "Order") +
        geom_bar(stat = "identity", position = "stack") +
        scale_fill_manual(values = my_colors) +
        theme(
            axis.text.x = element_text(
                angle = 85,
                hjust = 1,
                vjust = 1,
                size  = 5,
                colour = "black"
            ),
            axis.text.y = element_text(size = 7, colour = "black"),
            legend.position = "bottom"
        ) +
        guides(fill = guide_legend(nrow = 4))
```

### Aggregate samples by group and normalize read counts within each group to correct for differences in sequencing depth.

```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps_abund_rel_group_, fill="Order") + geom_bar(aes(), stat="identity", position="stack") + scale_x_discrete(limits = group_order) +
    scale_fill_manual(values = my_colors) + labs(x = "Group") + theme(axis.text = element_text(angle = 0, size = 10, colour="black"), axis.text.x = element_text(angle = -15),hjust = 10,vjust = 2)
```
\pagebreak

## 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_abund_rel, x = "SampleID", fill = "Family") +
        geom_bar(stat = "identity", position = "stack") +
        scale_fill_manual(values = my_colors) +
        theme(
            axis.text.x = element_text(
                angle = 85,
                hjust = 1,
                vjust = 1,
                size  = 5,
                colour = "black"
            ),
            axis.text.y = element_text(size = 7, colour = "black"),
            legend.position = "bottom"
        ) +
        guides(fill = guide_legend(nrow = 8))
```

### Aggregate samples by group and normalize read counts within each group to correct for differences in sequencing depth.

```{r, echo=TRUE, warning=FALSE}
    plot_bar(ps_abund_rel_group_, fill="Family") + geom_bar(aes(), stat="identity", position="stack") + scale_x_discrete(limits = group_order) +
    scale_fill_manual(values = my_colors) + labs(x = "Group") + theme(axis.text = element_text(angle = 0, size = 10, colour="black"), axis.text.x = element_text(angle = -15),hjust = 10,vjust = 2)
```
\pagebreak

```{r, fig.width=16, fig.height=8, echo=FALSE, warning=FALSE}
# !!!!NOT_USED!!!!: #Export Relative abundances of Phylum, Class, Order, and Family levels across all samples in Excel files!
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_abund_rel
col_sums <- colSums(otu_table(ps_abund_rel))
cat("Column sums of ps_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_abund_rel, "Phylum", "relative_abundance_phylum_old.xlsx")
#save_taxa_abundance(ps_abund_rel, "Class", "relative_abundance_class_old.xlsx")
#save_taxa_abundance(ps_abund_rel, "Order", "relative_abundance_order_old.xlsx")
#save_taxa_abundance(ps_abund_rel, "Family", "relative_abundance_family_old.xlsx")
```

```{r, fig.width=16, fig.height=8, echo=FALSE, 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_abund_rel
col_sums <- colSums(otu_table(ps_abund_rel))
cat("Column sums of ps_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_abund_rel, "Phylum", "relative_abundance_phylum.xlsx")
#save_taxa_abundance(ps_abund_rel, "Class", "relative_abundance_class.xlsx")
#save_taxa_abundance(ps_abund_rel, "Order", "relative_abundance_order.xlsx")
#save_taxa_abundance(ps_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;
```

\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
#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}
hmp.meta <- meta(ps_rarefied)
hmp.meta$sam_name <- rownames(hmp.meta)

# ---- enforce Group order (edit if you have different labels) ----
group_order <- c("1","2","3","4","5","negative control", "positive control")
hmp.meta$Group <- factor(as.character(hmp.meta$Group), levels = group_order)

# for QIIME2: Lesen der Metriken
shannon <- read.table("exported_alpha/shannon/alpha-diversity.tsv", header=TRUE, sep="\t")  #cp -r ../Data_Karoline_16S_2025/exported_alpha/ .
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))
p2 <- p +
stat_compare_means(
    method="t.test",
    comparisons = list(c("1", "2"), c("1", "3"), c("1", "4"), c("1", "5"), c("2", "3"), c("2", "4"), c("2", "5"), c("3", "4"), c("3", "5"), c("4", "5")),
    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
ggsave("./figures/alpha_diversity_Group.png", device="png", height = 10, width = 15)
ggsave("./figures/alpha_diversity_Group.svg", device="svg", height = 10, width = 15)
```

```{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"))
```

```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#!!# Beta diversity (Bray-Curtis distance)
#!!## Group1 vs Group2

#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")

```

# Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity

Global PERMANOVA on the weighted UniFrac distance matrix indicated a significant effect of Group on overall community composition (adonis2: R² = 0.1606, F = 7.397, p = 1×10⁻⁴; 9,999 permutations).

```{r, echo=FALSE, results='asis'}
# --- Global beta-diversity (PERMANOVA) ---
cat("```text\n")
cat(
"[PERMANOVA result]\n",
"The object contained internal attribute: PCoA ADONIS\n",
"Permutation test for adonis under reduced model\n",
"Permutation: free\n",
"Number of permutations: 9999\n\n",
"vegan::adonis2(formula = .formula, data = sampleda, permutations = permutations, method = distmethod)\n",
"          Df SumOfSqs      R2      F Pr(>F)\n",
"Model      6   11.446 0.16058 7.3971  1e-04 ***\n",
"Residual 232   59.829 0.83942\n",
"Total    238   71.274 1.00000\n",
"---\n",
"Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
sep = ""
)
cat("```\n")
```

Pairwise PERMANOVA tests were performed on Bray–Curtis distance matrices to compare bacterial community composition between all pairs of sample groups (metadata column Group). For each pairwise comparison, the distance matrix was subset to samples from the two groups only, and significance was assessed using vegan::adonis2 with 9,999 permutations. Resulting p-values were adjusted for multiple testing using both Benjamini–Hochberg (BH/FDR) and Bonferroni corrections.

```{r, echo=FALSE, warning=FALSE, out.width = '100%', fig.align= "center"}
#, and the full results were exported to figures/Bray_pairwise_PERMANOVA.csv
# --- Pairwise PERMANOVA results ---

Bray_pairwise_PERMANOVA <- read.csv("figures/Bray_pairwise_PERMANOVA.csv", sep = ",")
knitr::kable(Bray_pairwise_PERMANOVA, caption = "Pairwise PERMANOVA results (distance-based community differences among Group levels).") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

# --- Ordination figures ---

#knitr::include_graphics("./PCoA.png")
knitr::include_graphics("./PCoA2.png")
knitr::include_graphics("./PCoA3.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.

```{r, echo=FALSE, warning=FALSE}
# ------------------------------------------------------------
#  DESeq2: non-rarefied integer counts + optional taxon prefilter
# ------------------------------------------------------------
ps_deseq <- ps_filt

Group1<-c("U24080201","U25020701","O23092004","U24101801","U25022101","O23102703","A24062801","O23112205","U23071901","A23112002","U24111801","O23110101","U24121801","O23120101","O24011202","O23090803","A23060602","A24030402","U25011701","O24011901","U23090801","O24011201","O24011003","O23092202","O23082301","O23091403","O23112901","O23092201","O24013103","O24021403","O24010402","O23092005","O23092203","O24010302","O23090701","O23091501","O23092701","O24022202","O23092802","O23090601","O23100401","O24022102","O23081801","O23092006","O23100503","O23090602","O24013104","O24020803","O24010301","O24010404","O23090802","O23092801","O24022801","O23100706","O23102602","O24021601","O24012401","O24021603","O24022901","O24021501","O23110902","O23102601","O23102704","O23100803","O23102701","O24021401","O24022101","O24030603","O23110901","O23110903","O23110301","O24022301","O23102502","O23111501","O23111602","O24020705","O24021502","O24022201","O23110202","O23090801")
Group2<-c("U23071701","U23052401","U23052201","U24070401","O24011801","O23092003","A24071901","A24072901","O24011102","O23121501","O23092104","O23092001","O23121301","O24020701","O23112201","O23100701","O23100801","O24020903","O24020901","O24020703","O23112204")
Group3<-c("O23100802","O24011205","O23092002","O24011207","O23092103","O23102501","O24011005","A24030401","O24011004","A23051102","U25011702","O24011204","O23121502","O23120702","O24011206","O24021404","O23092101","O24010403","O23112303","O23083001","O23082302","O24010401","O24022302","O24010501","O23112902","O23082303","O23083102","O24013101","O23100402","O24020801","O23120701","O23121304","O24021602","O24011802","O23121306","O23120103","O24020905","O24012403","O24013102","O24021503","O24020904","O23102504","O24013105","O24030601","O23100705","O24030604","O23111601","O24020103","O24030602","O23110302","O23102603","O24031304","O24021402","O24020101","O24012501","O24020804","O23100804","O23102503","O24022902","O24020704","O23110904","O24020102","O24012402","O23102702","O23102604","O23110204","O23110203","O23083101","O23092702")
Group4<-c("O23112304","A23051103","A24071701","A23080101","A24031201","A24080201","O24011105","O23091305","O23121302","O23092803")
Group5<-c("O23091303","O23112301","O24011203","A23112001","O24011001","O24011002","O23091302","O24020902","O23092102","O23091401","O23121503","O23091402","O24020702","O23091301","O23112206","O24011103","O23121305","O24011101","O23121303","O24011104","O23120104","O23100704","O23112302","O23112203","O23100703","O24020805","O24020802","O23112202","O24031302","O23111502","O23100702","O24031301","O24031305","O23082402")
NTC<-c("NTC_2","NTC_3","NTC_16","NTC_4","NTC_6","NTC_7","NTC_8","NTC_9","NTC_11","NTC_12","NTC_13","NTC_14","NTC_15","NTC_10")
PC<-c("PC_1","PC_2","PC_8","PC_4","PC_3","PC_5","PC_6","PC_7","UR009768","UR009909","PC01")
```

## Group 1 vs Group 2

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group1,Group2)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "2")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group1_vs_Group2"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 1 vs Group 3

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group1,Group3)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "3")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group1_vs_Group3"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 1 vs Group 4

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group1,Group4)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "4")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group1_vs_Group4"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 1 vs Group 5

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group1,Group5)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "5")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group1_vs_Group5"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 2 vs Group 3

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group2,Group3)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "3")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group2_vs_Group3"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 2 vs Group 4

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group2,Group4)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "4")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group2_vs_Group4"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 2 vs Group 5

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group2,Group5)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "5")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group2_vs_Group5"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 3 vs Group 4

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group3,Group4)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "4")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group3_vs_Group4"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 3 vs Group 5

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group3,Group5)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "5")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group3_vs_Group5"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

## Group 4 vs Group 5

```{r, echo=TRUE, warning=FALSE}
ps_deseq_sel <- data.table::copy(ps_deseq)
otu_table(ps_deseq_sel) <- otu_table(ps_deseq)[,c(Group4,Group5)]
diagdds = phyloseq_to_deseq2(ps_deseq_sel, ~Group)
diagdds$Group <- relevel(diagdds$Group, "5")
diagdds <- DESeq(
    diagdds,
    test   = "Wald",
    fitType = "parametric",
    sfType  = "poscounts"  # <- important
)
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_deseq_sel)[rownames(sigtab), ], "matrix"))
# file base name
fname <- "DEGs_Group4_vs_Group5"
write.xlsx(sigtab, file = paste0(fname, ".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))
# build the plot
p <- ggplot(sigtab, aes(x = log2FoldChange, y = Family, color = Order)) +
    geom_point(aes(size = padj)) +
    scale_size_continuous(name = "padj", range = c(8, 4)) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust = 0.5))
# SVG (svglite gives crisp text)
if (!requireNamespace("svglite", quietly = TRUE)) install.packages("svglite")
ggplot2::ggsave(paste0(fname, ".svg"), plot = p, device = svglite::svglite,
                                width = 8, height = 6, units = "in", dpi = 300)
# PNG
ggplot2::ggsave(paste0(fname, ".png"), plot = p, device = "png",
                                width = 8, height = 6, units = "in", dpi = 300)
knitr::include_graphics(paste0(fname, ".png"))
```

1. Install and test qiime2-docker

   #Cannot run under QIIME1, switch to QIIME2: pick_open_reference_otus.py -r/home/jhuang/REFs/SILVA_132_QIIME_release/rep_set/rep_set_16S_only/99/silva_132_99_16S.fna -i test.fna -o clustering_test/ -p clustering_params.txt --parallel --verbose
   
   docker pull quay.io/qiime2/core:2023.9
   
   docker run -it --rm \
   -v /mnt/md1/DATA/Data_Childrensclinic_16S_2025:/data \
   -v /home/jhuang/REFs:/home/jhuang/REFs \
   quay.io/qiime2/core:2023.9 bash
   cd /data

2. Import the fastq-files to paired-end-demux.qza

   #https://docs.qiime2.org/2018.8/tutorials/importing/
   
   #Moving the following fastq.gz to raw_data_NOT_USED:
   643805249,Kinderklinik Lauf 2 Pl.B,B04,N720-B,CGGAGCCT,S503-B,TATCCTCT,nan,nan
   7909160377,Kinderkllinik Lauf 2 Pl.D,D11,N728-D,TGCAGCTA,S517-D,GCGTAAGA,nan,nan
   7909188256,Kinderklinik Lauf 2 Pl.C,C10,N712-C,GTAGAGGA,S516-C,CCTAGAGT,nan,nan
   O231000101,Kinderkllinik Lauf 2 Pl.D,F11,N728-D,TGCAGCTA,S520-D,AAGGCTAT,nan,nan
   O231000102,Kinderkllinik Lauf 2 Pl.D,C09,N726-D,CCTAAGAC,S516-D,CCTAGAGT,nan,nan
   O231000103,Kinderkllinik Lauf 2 Pl.D,C11,N728-D,TGCAGCTA,S516-D,CCTAGAGT,nan,nan
   O231000902,Kinderkllinik Lauf 2 Pl.D,C12,N729-D,TCGACGTC,S516-D,CCTAGAGT,nan,nan
   O23100201,Kinderklinik Lauf 2 Pl.B,D10,N727-B,CGATCAGT,S506-B,ACTGCATA,nan,nan
   O23100805,Kinderklinik Lauf 2 Pl.B,F10,N727-B,CGATCAGT,S508-B,CTAAGCCT,nan,nan
   O23100901,Kinderkllinik Lauf 2 Pl.D,F08,N724-D,ACTGAGCG,S520-D,AAGGCTAT,nan,nan
   O23100903,Kinderkllinik Lauf 2 Pl.D,B08,N724-D,ACTGAGCG,S515-D,TTCTAGCT,nan,nan
   O23100904,Kinderkllinik Lauf 2 Pl.D,C08,N724-D,ACTGAGCG,S516-D,CCTAGAGT,nan,nan
   O23100905,Kinderkllinik Lauf 2 Pl.D,D10,N727-D,CGATCAGT,S517-D,GCGTAAGA,nan,nan
   O23111503,Kinderkllinik Lauf 2 Pl.D,A12,N729-D,TCGACGTC,S513-D,TCGACTAG,nan,nan
   O23120902,Kinderklinik Lauf 2 Pl.C,C03,N703-C,AGGCAGAA,S516-C,CCTAGAGT,nan,nan
   O24022002,Kinderklinik Lauf 2 Pl.C,H12,N715-C,ATCTCAGG,S522-C,TTATGCGA,nan,nan
   U23071801,Kinderklinik Lauf 2 Pl.B,B02,N718-B,GGAGCTAC,S503-B,TATCCTCT,nan,nan
   
   rename -n 's/^([A-Z]\d+)_S\d+_L\d+_R([12])_001\.fastq\.gz$/$1_R$2.fastq.gz/' *.fastq.gz
   rename 's/^([A-Z]\d+)_S\d+_L\d+_R([12])_001\.fastq\.gz$/$1_R$2.fastq.gz/' *.fastq.gz
   
   #for file in *.fastq.gz; do echo "mv $file $(echo $file | cut -d'_' -f1 | cut -d'-' -f1-1)_$(echo $file | cut -d'_' -f4).fastq.gz"; done
   for file in *.fastq.gz; do echo "mv $file $(echo $file | cut -d'_' -f1)_$(echo $file | cut -d'_' -f4).fastq.gz"; done
   #MANUALLY correct several filename errors and performing in the generated commands above
   #MANUALLY correct several filename errors and performing in the generated commands below
   
   NTC_2
   NTC_3
   NTC_4
   NTC_5
   NTC_6
   NTC_7
   NTC_8
   NTC_9
   NTC_10
   NTC_11
   NTC_12
   NTC_13
   NTC_14
   NTC_15
   NTC_16
   
   mv NTC-1_R1.fastq.gz NTC_1_R1.fastq.gz
   mv NTC-1_R2.fastq.gz NTC_1_R2.fastq.gz
   mv NTC-2_R1.fastq.gz NTC_2_R1.fastq.gz
   mv NTC-2_R2.fastq.gz NTC_2_R2.fastq.gz
   mv NTC-3_R1.fastq.gz NTC_3_R1.fastq.gz
   mv NTC-3_R2.fastq.gz NTC_3_R2.fastq.gz
   mv NTC-4_R1.fastq.gz NTC_4_R1.fastq.gz
   mv NTC-4_R2.fastq.gz NTC_4_R2.fastq.gz
   mv NTC-5_R1.fastq.gz NTC_5_R1.fastq.gz
   mv NTC-5_R2.fastq.gz NTC_5_R2.fastq.gz
   mv NTC-6_R1.fastq.gz NTC_6_R1.fastq.gz
   mv NTC-6_R2.fastq.gz NTC_6_R2.fastq.gz
   mv NTC-7_R1.fastq.gz NTC_7_R1.fastq.gz
   mv NTC-7_R2.fastq.gz NTC_7_R2.fastq.gz
   mv NTC-8_R1.fastq.gz NTC_8_R1.fastq.gz
   mv NTC-8_R2.fastq.gz NTC_8_R2.fastq.gz
   mv NTC-9_R1.fastq.gz NTC_9_R1.fastq.gz
   mv NTC-9_R2.fastq.gz NTC_9_R2.fastq.gz
   mv NTC-10_R1.fastq.gz NTC_10_R1.fastq.gz
   mv NTC-10_R2.fastq.gz NTC_10_R2.fastq.gz
   mv NTC-11_R1.fastq.gz NTC_11_R1.fastq.gz
   mv NTC-11_R2.fastq.gz NTC_11_R2.fastq.gz
   mv NTC-12_R1.fastq.gz NTC_12_R1.fastq.gz
   mv NTC-12_R2.fastq.gz NTC_12_R2.fastq.gz
   mv NTC-13_R1.fastq.gz NTC_13_R1.fastq.gz
   mv NTC-13_R2.fastq.gz NTC_13_R2.fastq.gz
   mv NTC-14_R1.fastq.gz NTC_14_R1.fastq.gz
   mv NTC-14_R2.fastq.gz NTC_14_R2.fastq.gz
   mv NTC-15_R1.fastq.gz NTC_15_R1.fastq.gz
   mv NTC-15_R2.fastq.gz NTC_15_R2.fastq.gz
   mv NTC-16_R1.fastq.gz NTC_16_R1.fastq.gz
   mv NTC-16_R2.fastq.gz NTC_16_R2.fastq.gz
   
   PC_1
   PC_2
   PC_3
   PC_4
   PC_5
   PC_6
   PC_7
   PC_8
   
   mv PC-1_R1.fastq.gz PC_1_R1.fastq.gz
   mv PC-1_R2.fastq.gz PC_1_R2.fastq.gz
   mv PC-2_R1.fastq.gz PC_2_R1.fastq.gz
   mv PC-2_R2.fastq.gz PC_2_R2.fastq.gz
   mv PC-3_R1.fastq.gz PC_3_R1.fastq.gz
   mv PC-3_R2.fastq.gz PC_3_R2.fastq.gz
   mv PC-4_R1.fastq.gz PC_4_R1.fastq.gz
   mv PC-4_R2.fastq.gz PC_4_R2.fastq.gz
   mv PC-5_R1.fastq.gz PC_5_R1.fastq.gz
   mv PC-5_R2.fastq.gz PC_5_R2.fastq.gz
   mv PC-6_R1.fastq.gz PC_6_R1.fastq.gz
   mv PC-6_R2.fastq.gz PC_6_R2.fastq.gz
   mv PC-7_R1.fastq.gz PC_7_R1.fastq.gz
   mv PC-7_R2.fastq.gz PC_7_R2.fastq.gz
   mv PC-8_R1.fastq.gz PC_8_R1.fastq.gz
   mv PC-8_R2.fastq.gz PC_8_R2.fastq.gz
   
   #MOVE the used fastq.gz to the directory raw_data
   #PREPARE the conf-file --> "-rw-rw-r-- 1 jhuang jhuang 9,3K Nov 20 12:22  pe-33-manifest"
   qiime tools import --type 'SampleData[PairedEndSequencesWithQuality]' --input-path pe-33-manifest --output-path paired-end-demux.qza --input-format PairedEndFastqManifestPhred33
   #--> "-rw-r--r-- 1 root   root   3,5G Nov 20 12:24  paired-end-demux.qza"
   
   qiime demux summarize \
   --i-data paired-end-demux.qza \
   --o-visualization demux_pe.qzv
   #--> "-rw-r--r-- 1 root   root   320K Nov 20 12:34  demux_pe.qzv"
   
   #qiime tools view demux_pe.qzv --> Error: Visualization viewing is currently not supported in headless environments. You can view Visualizations (and Artifacts) at https://view.qiime2.org, or move the Visualization to an environment with a display and view it with `qiime tools view`.
   #Open demux_pe.gzv on https://view.qiime2.org

3. Optimizing the parameters trunc-len-f and trunc-len-r and denoising with DADA2: optimized parameters is f240_r240

   #Your amplicon (V3–V4 region) is ~464 bp, so you need ≥20–30 bp overlap
   #464-38=426; 440 is the longst +12 nt for overlapping=we need 452 nt!
   
   #Optimize the parameters --p-trunc-len-f and --p-trunc-len-r
   (qiime2-amplicon-2023.9) root@4379fea45cf7:/data# ./dada2_batch_test.sh
   
           #!/bin/bash
   
           # Set your base inputs
           INPUT=paired-end-demux.qza
           TRIM_LEFT_F=17
           TRIM_LEFT_R=21
   
           # Output base
           OUTPUT_DIR=dada2_tests
           mkdir -p $OUTPUT_DIR
   
           # Loop over trunc-len-f and trunc-len-r combinations
           # Forward: from 220 to 260
           # Reverse: from 210 to 260
           #225 220 215
           i=1
           for f in 260 255 250 245 240 235 230 225 220; do
                   for r in 260 255 250 245 240 235 230 225 220 215 210; do
                   OUT=test_${i}_f${f}_r${r}
                   echo "Running: $OUT"
                   mkdir -p $OUTPUT_DIR/$OUT
   
                   qiime dada2 denoise-paired \
                   --i-demultiplexed-seqs $INPUT \
                   --p-trim-left-f $TRIM_LEFT_F \
                   --p-trim-left-r $TRIM_LEFT_R \
                   --p-max-ee-f 3 --p-max-ee-r 5 \
                   --p-trunc-len-f $f \
                   --p-trunc-len-r $r \
                   --p-n-threads 32 \
                   --o-table $OUTPUT_DIR/$OUT/table.qza \
                   --o-representative-sequences $OUTPUT_DIR/$OUT/rep-seqs.qza \
                   --o-denoising-stats $OUTPUT_DIR/$OUT/denoising-stats.qza \
                   --verbose > $OUTPUT_DIR/$OUT/log.txt 2>&1
   
                   ((i++))
                   done
           done
   
   for f in dada2_tests2/test_*/denoising-stats.qza; do
   qiime metadata tabulate \
       --m-input-file $f \
       --o-visualization ${f%.qza}.qzv
   done
   
   #Manually convert denoising-stats.qza to denoising-stats.tsv using https://view.qiime2.org
   #Downloads to jhuang@WS-2290C:~/DATA/Data_Childrensclinic_16S_2025/dada2_tests/test_59_f235_r245$ mv ~/Downloads/data_stats.tsv .
   #NTC01  33463   1732    5.18    1723    1650    4.93    1589    4.75
   #O23091304  54831   7477    13.64   7409    7133    13.01   7101    12.95
   #NTC01  33463   1719    5.14    1711    1648    4.92    1588    4.75
   #O23091304  54831   7419    13.53   7345    7068    12.89   7046    12.85
   
   #pandaseq.out: grep ">" A1_R1.fastq.gz_merged.fasta | wc -l #8229;  grep ">" A10_R1.fastq.gz_merged.fasta | wc -l #9165
   
   sudo chown -R jhuang:jhuang dada2_tests
   cd dada2_tests
   python3 rank_dada2_params.py
   
   # Top parameter sets by % input non-chimeric (overall):
   # test_69_f230_r250: f=230, r=250, %non-chimeric=81.75, median%=84.57, merged%=83.86, samples_nonchim=253/253
   # * (CHOOSEN) test_59_f235_r245: f=235, r=245, %non-chimeric=81.57, median%=84.46, merged%=83.79, samples_nonchim=253/253
   # test_58_f235_r250: f=235, r=250, %non-chimeric=81.49, median%=84.35, merged%=83.61, samples_nonchim=253/253
   # test_49_f240_r240: f=240, r=240, %non-chimeric=81.30, median%=84.02, merged%=83.46, samples_nonchim=253/253
   
   # test_48_f240_r245: f=240, r=245, %non-chimeric=81.21, median%=83.95, merged%=83.33, samples_nonchim=253/253
   # test_47_f240_r250: f=240, r=250, %non-chimeric=81.12, median%=83.94, merged%=83.19, samples_nonchim=253/253
   # test_39_f245_r235: f=245, r=235, %non-chimeric=80.84, median%=83.59, merged%=82.98, samples_nonchim=253/253
   # test_38_f245_r240: f=245, r=240, %non-chimeric=80.78, median%=83.52, merged%=82.91, samples_nonchim=253/253
   # test_37_f245_r245: f=245, r=245, %non-chimeric=80.72, median%=83.36, merged%=82.81, samples_nonchim=253/253
   # test_36_f245_r250: f=245, r=250, %non-chimeric=80.62, median%=83.41, merged%=82.68, samples_nonchim=253/253

4. Visualize outputs (Using pandaseq.out, since qiime2_metadata.tsv contains the pathway of pandaseq.out)

   4.1. mkdir fastqc_out fastqc -t 4 raw_data/* -o fastqc_out/ 4.2. mkdir trim_data trimmed_unpaired cd raw_data for file in O24021602_R1.fastq.gz O24021601_R1.fastq.gz O23100705_R1.fastq.gz PC_5_R1.fastq.gz O24010402_R1.fastq.gz U23090801_R1.fastq.gz O24012402_R1.fastq.gz NTC_7_R1.fastq.gz O23121301_R1.fastq.gz O24013104_R1.fastq.gz U25020701_R1.fastq.gz O23100401_R1.fastq.gz O24020901_R1.fastq.gz NTC_1_R1.fastq.gz O24020903_R1.fastq.gz O23092002_R1.fastq.gz UR009768_R1.fastq.gz O23100402_R1.fastq.gz O23091304_R1.fastq.gz O23100703_R1.fastq.gz O23110904_R1.fastq.gz O23110903_R1.fastq.gz O23121304_R1.fastq.gz O23083101_R1.fastq.gz O23100502_R1.fastq.gz O23110301_R1.fastq.gz O24020805_R1.fastq.gz A23051102_R1.fastq.gz O23102604_R1.fastq.gz A23051103_R1.fastq.gz O23102504_R1.fastq.gz O24011205_R1.fastq.gz O23111602_R1.fastq.gz O24020905_R1.fastq.gz O23090701_R1.fastq.gz O24020702_R1.fastq.gz NTC_4_R1.fastq.gz A24072901_R1.fastq.gz NTC_10_R1.fastq.gz O23100704_R1.fastq.gz NTC_8_R1.fastq.gz O24011901_R1.fastq.gz O23111501_R1.fastq.gz O23112303_R1.fastq.gz O23112902_R1.fastq.gz O24011203_R1.fastq.gz O23092006_R1.fastq.gz O24010404_R1.fastq.gz O23090803_R1.fastq.gz O23091501_R1.fastq.gz U25022101_R1.fastq.gz O23121305_R1.fastq.gz U24080201_R1.fastq.gz U24111801_R1.fastq.gz A24040201_R1.fastq.gz O23120702_R1.fastq.gz NTC_5_R1.fastq.gz O24013105_R1.fastq.gz O23100706_R1.fastq.gz O23083102_R1.fastq.gz O23091305_R1.fastq.gz PC_7_R1.fastq.gz O24011005_R1.fastq.gz PC01_R1.fastq.gz O24011004_R1.fastq.gz O23110902_R1.fastq.gz O24020904_R1.fastq.gz A24071701_R1.fastq.gz O24021501_R1.fastq.gz O23120101_R1.fastq.gz U25011702_R1.fastq.gz O23092103_R1.fastq.gz UR009909_R1.fastq.gz O23091403_R1.fastq.gz NTC_2_R1.fastq.gz PC_1_R1.fastq.gz O24013102_R1.fastq.gz O23102704_R1.fastq.gz O24010401_R1.fastq.gz O24012501_R1.fastq.gz U24121801_R1.fastq.gz O24031304_R1.fastq.gz O23090802_R1.fastq.gz NTC_12_R1.fastq.gz O23100702_R1.fastq.gz O24011202_R1.fastq.gz O23100601_R1.fastq.gz O23100802_R1.fastq.gz O24021404_R1.fastq.gz O23112301_R1.fastq.gz O24011103_R1.fastq.gz O24030601_R1.fastq.gz U23052201_R1.fastq.gz O23100803_R1.fastq.gz O23102502_R1.fastq.gz O24011206_R1.fastq.gz O23091401_R1.fastq.gz O23092001_R1.fastq.gz O24020801_R1.fastq.gz O23082302_R1.fastq.gz O23112204_R1.fastq.gz O23100701_R1.fastq.gz O23112304_R1.fastq.gz A24031201_R1.fastq.gz O24022801_R1.fastq.gz PC_3_R1.fastq.gz O24022901_R1.fastq.gz O23092005_R1.fastq.gz O23121502_R1.fastq.gz O24021401_R1.fastq.gz U25011701_R1.fastq.gz NTC01_R1.fastq.gz O23082401_R1.fastq.gz O24022202_R1.fastq.gz NTC_6_R1.fastq.gz O24031302_R1.fastq.gz NTC_15_R1.fastq.gz NTC_13_R1.fastq.gz O24022302_R1.fastq.gz O23121303_R1.fastq.gz O24021502_R1.fastq.gz O24020704_R1.fastq.gz O23092202_R1.fastq.gz A23112002_R1.fastq.gz O23092803_R1.fastq.gz O23112203_R1.fastq.gz NTC_11_R1.fastq.gz O23091302_R1.fastq.gz A24062801_R1.fastq.gz O23121302_R1.fastq.gz O23092104_R1.fastq.gz O24020802_R1.fastq.gz O24021403_R1.fastq.gz O23112201_R1.fastq.gz O23082402_R1.fastq.gz U24070401_R1.fastq.gz O24020902_R1.fastq.gz O24030602_R1.fastq.gz NTC_3_R1.fastq.gz O24011201_R1.fastq.gz O23091301_R1.fastq.gz O23112202_R1.fastq.gz O24021503_R1.fastq.gz O23102601_R1.fastq.gz O24011002_R1.fastq.gz O23121503_R1.fastq.gz O23092004_R1.fastq.gz O23091402_R1.fastq.gz O23092701_R1.fastq.gz O24031301_R1.fastq.gz O24020703_R1.fastq.gz O24013103_R1.fastq.gz A24080201_R1.fastq.gz U23071201_R1.fastq.gz U23052401_R1.fastq.gz U23071901_R1.fastq.gz O24011101_R1.fastq.gz U23091101_R1.fastq.gz O23110204_R1.fastq.gz O23102702_R1.fastq.gz O24020101_R1.fastq.gz O24012401_R1.fastq.gz O23100501_R1.fastq.gz O24020705_R1.fastq.gz A23060602_R1.fastq.gz O23102701_R1.fastq.gz O24011001_R1.fastq.gz O24022102_R1.fastq.gz O23110302_R1.fastq.gz O23100503_R1.fastq.gz O24020803_R1.fastq.gz O23110203_R1.fastq.gz A24030402_R1.fastq.gz O23092102_R1.fastq.gz O24011003_R1.fastq.gz O23121306_R1.fastq.gz O23110101_R1.fastq.gz O24020804_R1.fastq.gz O23112901_R1.fastq.gz A23072501_R1.fastq.gz O24030604_R1.fastq.gz O23112302_R1.fastq.gz O24010301_R1.fastq.gz O24021402_R1.fastq.gz O24011104_R1.fastq.gz O23102602_R1.fastq.gz O23102703_R1.fastq.gz O23091303_R1.fastq.gz O24011204_R1.fastq.gz A24071901_R1.fastq.gz O23102603_R1.fastq.gz O23082303_R1.fastq.gz O24011801_R1.fastq.gz O24030603_R1.fastq.gz O23092802_R1.fastq.gz O23090801_R1.fastq.gz A24030401_R1.fastq.gz O23083001_R1.fastq.gz A23060601_R1.fastq.gz O23092203_R1.fastq.gz NTC_14_R1.fastq.gz O23090602_R1.fastq.gz A23111301_R1.fastq.gz O23120103_R1.fastq.gz O24011105_R1.fastq.gz U23071701_R1.fastq.gz O23111601_R1.fastq.gz O24010501_R1.fastq.gz O23110202_R1.fastq.gz O23121501_R1.fastq.gz O24010302_R1.fastq.gz O24011207_R1.fastq.gz O23092702_R1.fastq.gz O23112206_R1.fastq.gz PC_2_R1.fastq.gz O23102503_R1.fastq.gz O23092201_R1.fastq.gz NTC_9_R1.fastq.gz A23080101_R1.fastq.gz PC_6_R1.fastq.gz O23120701_R1.fastq.gz U24101801_R1.fastq.gz A23112001_R1.fastq.gz O23120104_R1.fastq.gz O23100804_R1.fastq.gz O24031305_R1.fastq.gz O23111502_R1.fastq.gz O24020103_R1.fastq.gz O23082301_R1.fastq.gz NTC_16_R1.fastq.gz PC_8_R1.fastq.gz O24022101_R1.fastq.gz O24020102_R1.fastq.gz O23081801_R1.fastq.gz O24022902_R1.fastq.gz O23112205_R1.fastq.gz O24021603_R1.fastq.gz O23102501_R1.fastq.gz O24010403_R1.fastq.gz O24012403_R1.fastq.gz O23092003_R1.fastq.gz O24013101_R1.fastq.gz O24011802_R1.fastq.gz O24011102_R1.fastq.gz PC_4_R1.fastq.gz O23100801_R1.fastq.gz O23092101_R1.fastq.gz O24020701_R1.fastq.gz O23092801_R1.fastq.gz O24022201_R1.fastq.gz O23110901_R1.fastq.gz O23090601_R1.fastq.gz O24022301_R1.fastq.gz; do java -jar /home/jhuang/Tools/Trimmomatic-0.36/trimmomatic-0.36.jar PE -threads 16 $file ${file/_R1/_R2} ../trim_data/$file ../trimmed_unpaired/$file ../trim_data/${file/_R1/_R2} ../trimmed_unpaired/${file/_R1/_R2} 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

   4.3. mkdir pandaseq.out conda activate /home/jhuang/miniconda3/envs/qiime1 for file in trim_data/*_R1.fastq.gz; do pandaseq -f ${file} -r ${file/_R1.fastq.gz/R2.fastq.gz} -l 300 -p CCTACGGGNGGCWGCAG -q GACTACHVGGGTATCTAATCC -w pandaseq.out/$(echo $file | cut -d’/’ -f2 | cut -d’‘ -f1-3)_merged.fasta >> LOG_pandaseq; done conda deactivate

   4.4. prepare qiime2_metadata.tsv

   4.5. run qiime feature-table summarize \ –i-table dada2_tests/test_59_f235_r245/table.qza \ –o-visualization table.qzv \ –m-sample-metadata-file qiime2_metadata.tsv

   #Table summary
   #Metric Sample
   #Number of samples  137-->96
   #Number of features 3,039-->21,893
   #Total frequency    1,641,484-->9,246,546
   #
   #Frequency per sample
   #Minimum frequency  413.0-->41,764.0
   #1st quartile   10,319.0-->100,017.5
   #Median frequency   11,530.0-->100,017.5
   #3rd quartile   13,146.0-->110,183.5
   #Maximum frequency  40,022.0-->143,563.0
   #Mean frequency 11,981.635036496351-->96,318.1875
   #
   #Frequency per feature
   #Minimum frequency  1.0
   #1st quartile   3.0
   #Median frequency   8.0-->4.0
   #3rd quartile   95.5-->14.0
   #Maximum frequency  56,472.0-->983,499.0
   #Mean frequency 540.1395195788089-->422.35
   
   #qiime tools peek dada2_tests/test_59_f235_r245/table.qza
   #qiime tools peek qiime2_metadata.tsv
   
   qiime feature-table tabulate-seqs \
   --i-data dada2_tests/test_59_f235_r245/rep-seqs.qza \
   --o-visualization rep-seqs.qzv
   
   qiime metadata tabulate \
   --m-input-file dada2_tests/test_59_f235_r245/denoising-stats.qza \
   --o-visualization denoising-stats.qzv

5. Import reference sequences and taxonomy (SILVA 132)

   qiime tools import \
   --type 'FeatureData[Sequence]' \
   --input-path /home/jhuang/REFs/SILVA_132_QIIME_release/rep_set/rep_set_16S_only/99/silva_132_99_16S.fna \
   --output-path silva_132_99_otus.qza \
   --input-format DNAFASTAFormat
   
   qiime tools import \
   --type 'FeatureData[Taxonomy]' \
   --input-format HeaderlessTSVTaxonomyFormat \
   --input-path /home/jhuang/REFs/SILVA_132_QIIME_release/taxonomy/16S_only/99/consensus_taxonomy_7_levels.txt \
   --output-path silva_132_99_taxonomy.qza

6. Assign taxonomy

   qiime feature-classifier classify-consensus-vsearch \
   --i-query  dada2_tests/test_59_f235_r245/rep-seqs.qza \
   --i-reference-reads silva_132_99_otus.qza \
   --i-reference-taxonomy silva_132_99_taxonomy.qza \
   --p-perc-identity 0.97 \
   --p-threads 64 \
   --o-classification taxonomy.qza \
   --o-search-results search-results.qza

7. Visualize taxonomy

   qiime taxa barplot \
   --i-table  dada2_tests/test_59_f235_r245/table.qza \
   --i-taxonomy taxonomy.qza \
   --m-metadata-file qiime2_metadata.tsv \
   --o-visualization taxa-bar-plots.qzv

8. Build phylogenetic tree

   qiime alignment mafft \
   --i-sequences  dada2_tests/test_59_f235_r245/rep-seqs.qza \
   --o-alignment aligned-rep-seqs.qza
   
   qiime alignment mask \
   --i-alignment aligned-rep-seqs.qza \
   --o-masked-alignment masked-aligned-rep-seqs.qza
   
   qiime phylogeny fasttree \
   --i-alignment masked-aligned-rep-seqs.qza \
   --o-tree unrooted-tree.qza
   
   # (*) The rooted-tree is generated from unrooted-tree, and will be used in the next step!
   qiime phylogeny midpoint-root \
   --i-tree unrooted-tree.qza \
   --o-rooted-tree rooted-tree.qza

9. Core diversity analysis

   #The -e 6389 flag sets the even sampling depth (rarefaction depth) to 6,389 reads for diversity analyses.
   #All samples will be rarefied to 4,753 reads.
   #Samples with fewer reads are excluded.
   qiime diversity core-metrics-phylogenetic \
   --i-phylogeny rooted-tree.qza \
   --i-table  dada2_tests/test_59_f235_r245/table.qza \
   --p-sampling-depth 6389 \
   --m-metadata-file qiime2_metadata.tsv \
   --output-dir core_metrics_results
   
   qiime diversity alpha \
   --i-table dada2_tests/test_59_f235_r245/table.qza \
   --p-metric chao1 \
   --o-alpha-diversity core_metrics_results/chao1_vector.qza
   
   qiime tools export --input-path core_metrics_results/shannon_vector.qza --output-path exported_alpha/shannon
   qiime tools export --input-path core_metrics_results/faith_pd_vector.qza --output-path exported_alpha/faith_pd
   qiime tools export --input-path core_metrics_results/observed_features_vector.qza --output-path exported_alpha/observed_features
   qiime tools export --input-path core_metrics_results/chao1_vector.qza --output-path exported_alpha/chao1
   
   qiime tools export \
   --input-path core_metrics_results/unweighted_unifrac_distance_matrix.qza \
   --output-path exported_unweighted_unifrac
   qiime tools export \
   --input-path core_metrics_results/weighted_unifrac_distance_matrix.qza \
   --output-path exported_weighted_unifrac
   
   qiime diversity beta-group-significance \
   --i-distance-matrix core_metrics_results/weighted_unifrac_distance_matrix.qza \
   --m-metadata-file qiime2_metadata.tsv \
   --m-metadata-column Group \
   --p-pairwise \
   --p-method permanova \
   --o-visualization beta_group_significance.qzv
   
   qiime tools export \
   --input-path beta_group_significance.qzv \
   --output-path exported_beta_group

10. Prepare three files feeding to Phyloseq.Rmd: table.qza (see above with ), rooted-tree.qza (see above with ), qiime2_metadata_for_qza_to_phyloseq.tsv edited from qiime2_metadata.tsv.

    # Rarefying can be performed here, or in Phyloseq.Rmd (default), therefore, we don't need this step any more.
    qiime feature-table summarize \
    --i-table core_metrics_results/rarefied_table.qza \
    --o-visualization rarefied_table.qzv \
    --m-sample-metadata-file qiime2_metadata.tsv
    
    #Table summary
    #Metric Sample
    #Number of samples  136
    #Number of features 2,781
    #Total frequency    868,904
    
    # In QIIME2, we need table.qza, not biom-file, therefore, we don't need this step any more.
    qiime tools export \
    --input-path core_metrics_results/rarefied_table.qza \
    --output-path exported_rarefied_table
    #--> exported_rarefied_table/feature-table.biom
    
    biom convert \
    -i exported_rarefied_table/feature-table.biom \
    -o exported_rarefied_table/feature-table.tsv \
    --to-tsv
    
    #✅ Old QIIME 1 table with GenBank IDs (like EF603722.1.1487) as feature labels.
    #✅ QIIME 2 table where feature IDs are hashes (like 0b438323a296b5f2ce2c8bbe3949ee8d).
    
    # Visulaize the taxonomy.qza
    qiime tools export \
    --input-path taxonomy.qza \
    --output-path exported-taxonomy
    
    #Feature ID    Taxon                               Confidence
    #0b4383...     k__Bacteria; p__Proteobacteria...   0.98
    #dfa833...     k__Bacteria; p__Firmicutes...       0.87
    #...
    
    # ---- I used the following to generate two file for feeding in the Phyloseq.Rmd ----
    
    #-1- exported_table/feature-table.biom corresesponds to table_even6389.biom in QIIME1, but in QIIME2, we don't need biom-file, instead of table.qza.
    
    qiime tools export \
    --input-path  dada2_tests/test_59_f235_r245/table.qza \
    --output-path exported_table
    #--> exported_table/feature-table.biom
    
    #-2- exported-tree/tree.nwk corresesponds to rep_set.tre in QIIME1
    
    qiime tools export \
    --input-path rooted-tree.qza \
    --output-path exported-tree
    #--> exported-tree/tree.nwk
    
    # END
    
    # ---- The code in Phyloseq.Rmd ----
    
    #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_tests/test_59_f235_r245/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")
    
    #Note that the alpha- and beta-diversity-files needed in Phyloseq.Rmd has been prepared in the step 9.

11. Figures generated by Phyloseq.Rmd and MicrobiotaProcess_*.R

    The following files can be found under server.

    ./Phyloseq.Rmd (Result Phyloseq.html)
    ./MicrobiotaProcess_cluster1_Group9-11_vs_cluster2_Group12-14_orig.R
    ./MicrobiotaProcess_Group1_vs_Group2.R
    ./MicrobiotaProcess_Group3_vs_Group4.R
    ./MicrobiotaProcess_PCA_Group1-4.R
    ./MicrobiotaProcess_PCA_Group9-14.R

12. Generating relative_abundance_phylum_order_family.xls Convert the Excel file for Phylum, Order, and Family relative abundances for all samples to csv file after correcting the family due to the two redundance species “=SUM(EG10+EG18)” Edit the csv file, merge then to a file.

    ~/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;

The two scripts largely use the same MicrobiotaProcess functions; the main difference is *which `ps_` object you feed in for each task** (alpha, beta, composition plots).

What the “small” script currently does

• Creates one MPSE (often called mpse_abund) from a taxa-filtered plotting object (e.g. ps.ng.tax_abund / your ps_abund) and then runs alpha diversity + beta diversity + composition plotting all on that same MPSE.
• It also uses rarefied abundance (RareAbundance) as the input for some taxonomy abundance plots/heatmaps.

Implication: alpha/beta diversity are being computed on a taxa-filtered dataset, which can:

• artificially reduce richness (Observed/Chao1),
• shift Shannon/Simpson,
• and change distance structure (especially for presence/absence metrics, but also sometimes Bray).

What the updated MicrobiotaProcess workflow (recommended) does

It splits the workflow into two MPSE objects, each built from the correct upstream ps_*:

1) Diversity MPSE (mpse_div)

Input: ps_filt (QC-filtered samples, full taxa set; not filtered “for plotting”)

• Alpha diversity: do rarefaction inside MPSE (mp_rrarefy()) and compute alpha on RareAbundance.
• Beta diversity (your current Bray+Hellinger): compute Hellinger from non-rarefied Abundance and then Bray/PCoA/PERMANOVA.

2) Plotting MPSE (mpse_plot)

Input: ps_abund_rel (taxa filtered for readability; relative abundance)

• Use this MPSE only for composition plots (stacked bars, heatmaps).

Implication: you keep diversity analyses biologically faithful (no “plotting filter”) while still producing clean, readable composition plots.

Other (non-critical) differences

• The updated script uses prune_samples() + prune_taxa() instead of overwriting otu_table() manually (safer / less error-prone).
• Outputs are consistently written into a figures/ directory.

Bottom line

Same MicrobiotaProcess functions; the updated script mainly fixes the *recommended `ps_` input choice** for each analysis type (diversity vs. plotting).