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Select Genes of Interest: Once Emilia provides the GO term, use a database like Gene Ontology or Bioconductor’s “org.Hs.eg.db” package (assuming human genes) to fetch the list of genes associated with that GO term.
# Using Bioconductor in R library(org.Mm.eg.db) genes <- select(org.Mm.eg.db, keys="GO:0048813", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") > genes GO EVIDENCE ONTOLOGY SYMBOL 1 GO:0048813 IDA BP Abi1 2 GO:0048813 IGI BP Abl2 3 GO:0048813 IMP BP Abl2 4 GO:0048813 IMP BP Atp7a 5 GO:0048813 IMP BP Cacna1a 6 GO:0048813 IMP BP Camk2a 7 GO:0048813 IMP BP Ctnna2 8 GO:0048813 IMP BP Cdk5 9 GO:0048813 IGI BP Dclk1 10 GO:0048813 IGI BP Dcx 11 GO:0048813 IMP BP Dscam 12 GO:0048813 IMP BP Dvl1 13 GO:0048813 IGI BP Fmn1 14 GO:0048813 IMP BP Fmn1 15 GO:0048813 IMP BP Fyn 16 GO:0048813 IMP BP Hprt 17 GO:0048813 IBA BP Sdc2 18 GO:0048813 IMP BP Elavl4 19 GO:0048813 IGI BP Itgb1 20 GO:0048813 IMP BP Itgb1 21 GO:0048813 IMP BP Lrp8 22 GO:0048813 ISO BP Mef2a 23 GO:0048813 IBA BP Map6 24 GO:0048813 ISO BP Map6 25 GO:0048813 IMP BP Slc11a2 26 GO:0048813 IGI BP Rac1 27 GO:0048813 IGI BP Rock2 28 GO:0048813 IMP BP Sema3a 29 GO:0048813 IMP BP Vldlr 30 GO:0048813 IMP BP Mapk8 31 GO:0048813 ISO BP Mink1 32 GO:0048813 IMP BP Tet1 33 GO:0048813 ISO BP Celsr2 34 GO:0048813 IMP BP Cacna1f 35 GO:0048813 IGI BP Dact1 36 GO:0048813 IMP BP Dact1 37 GO:0048813 IMP BP Mapk8ip2 38 GO:0048813 ISO BP Trak1 39 GO:0048813 IMP BP Rere 40 GO:0048813 ISO BP Trak2 41 GO:0048813 IBA BP Tmem106b 42 GO:0048813 ISO BP Tmem106b 43 GO:0048813 IMP BP Slitrk5 44 GO:0048813 IMP BP Kidins220 45 GO:0048813 IMP BP Rbfox2 46 GO:0048813 IMP BP Klf7 47 GO:0048813 IMP BP Dtnbp1 48 GO:0048813 IMP BP Prex2 49 GO:0048813 IBA BP Dcdc2a 50 GO:0048813 IGI BP Dcdc2a 51 GO:0048813 ISO BP Farp1 52 GO:0048813 ISO BP Lrp4 53 GO:0048813 IBA BP Btbd3 54 GO:0048813 IDA BP Btbd3 55 GO:0048813 IBA BP Abitram 56 GO:0048813 IMP BP Abitram 57 GO:0048813 IMP BP Picalm 58 GO:0048813 ISO BP Picalm -
Extract Promoter Regions: Typically, the promoter region can be defined as a certain number of base pairs (e.g., 1000-2000 bp) upstream of the transcription start site (TSS) of a gene. Tools like BEDTools can help you extract these regions from a genome reference.
2.1 extract annotation.gtf for mouse.
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UCSC Genome Browser: Navigate to the UCSC Table Browser. Choose the following settings: group: “Genes and Gene Predictions” track: “RefSeq Genes” (or another gene prediction track if you have a preference) output format: “GTF – gene transfer format” Click “get output”, and you can then save the result as a .gtf file.
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Ensembl: Go to the Ensembl FTP site. Navigate through: release-XYZ (choose the latest release) > gtf > mus_musculus (choose the appropriate assembly if there are multiple). Download the GTF file, which might be named something like Mus_musculus.GRCm38.XYZ.gtf.gz. Make sure to unzip the file after downloading. GENCODE:
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Visit the GENCODE Mouse website: https://www.gencodegenes.org/mouse/release_M25.html Download the comprehensive gene annotation GTF file for the mm10 assembly.
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Convert the GTF file to a BED file with only the TSS positions.
awk ‘OFS=”\t” {if ($3 == “gene”) print $1, $4, $4+1, $10, $7, $12, $14}’ gencode.vM25.annotation.gtf > tss.bed
grep “protein_coding” tss.bed | wc -l #21859 records
In many genomics studies, the promoter region of a gene is typically considered to be the sequence immediately upstream of the transcription start site (TSS). However, the exact definition of the “promoter region” can vary between studies and depends on the specific context.
For the mouse genome (mm10), a commonly used range for the promoter region is:
-2000 to +500 bp relative to the TSS: This defines a 2.5 kb region centered around the TSS. The 2 kb upstream often captures many of the cis-regulatory elements, while the 500 bp downstream includes the TSS and possibly the beginning of the gene. Some studies might define a narrower region, such as:
-1000 to +200 bp relative to the TSS Or even a broader one:
-5000 to +1000 bp relative to the TSS The best definition often depends on the specific research question. If the goal is to capture as many potential regulatory elements as possible, a broader range might be chosen. If the goal is to focus specifically on elements directly at the TSS, a narrower range might be more appropriate.
For many standard analyses, the -2000 to +500 bp window is a reasonable compromise that captures a significant portion of the regulatory elements without being overly broad. However, always consider the biological question and the specifics of your dataset when choosing the appropriate range for promoter regions.
#I used the two methods to extract promoter regions, why the result is different as shown below. #promoters.bed:chrM -498 2002 "ENSMUSG00000064336.1"; #promoters_.bed:chrM -999 1 "ENSMUSG00000064336.1"; # Convert TSS positions to promoter regions. Here, we're considering a promoter to be 1kb upstream of the TSS ($7 is SYMBOL, $4 ENSEMBL-ID). # For genes on the '+' strand: awk 'OFS="\t" {start=$2-2000>0?$2-2000:0; end=$2+500; if ($5 == "+") print $1, start, end, $4}' tss.bed > promoters_plus.bed # For genes on the '-' strand: awk 'OFS="\t" {start=$3-500>0?$3-500:0; end=$3+2000; if ($5 == "-") print $1, start, end, $4}' tss.bed > promoters_minus.bed # Combine the two promoter files. cat promoters_plus.bed promoters_minus.bed > promoters.bed # ERROR --> NEED to be DEBUGGED! # # Initialize an empty promoters.bed file # > promoters.bed # # Loop through each line of tss.bed # while IFS=$'\t' read -r chr start end name strand; do # if [[ $strand == "+" ]]; then # # Adjust for '+' strand genes, set minimum start to 0 # adjusted_start=$(( $start - 2000 > 0 ? $start - 2000 : 0 )) # echo -e "$chr\t$adjusted_start\t$(($start+500))\t$name" >> promoters.bed # else # # Adjust for '-' strand genes using the `end` column as the TSS, set minimum start to 0 # adjusted_start=$(( $end - 2500 > 0 ? $end - 2500 : 0 )) # echo -e "$chr\t$adjusted_start\t$(($end-1000))\t$name" >> promoters.bed # fi # done < tss.bed # # Extract sequences for these promoter regions. # bedtools getfasta -fi mm10_genome.fasta -bed promoters.bed -fo promoters.fasta # # Using bedtools. Assuming a BED file with TSS positions (tss.bed) # bedtools flank -i tss.bed -g mouse.genome -l 1000 -r 0 > promoters.bed -
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Quantify Methylation Signal in Promoters: With the promoter regions defined, use your bam files to calculate coverage or read depth over these regions. The depth can be interpreted as a signal of methylation. You can use tools such as bedtools coverage to get the depth of coverage over these promoter regions.
#Control females: bedtools coverage -a promoters.bed -b ../alg/1_0_1_sorted.bam > promoters_coverage_1.0.1.txt bedtools coverage -a promoters.bed -b ../alg/1_0_2_sorted.bam > promoters_coverage_1.0.2.txt bedtools coverage -a promoters.bed -b ../alg/1_0_3_sorted.bam > promoters_coverage_1.0.3.txt #Control males: bedtools coverage -a promoters.bed -b ../alg/1_1_2b_sorted.bam > promoters_coverage_1.1.2b.txt bedtools coverage -a promoters.bed -b ../alg/1_1_3_sorted.bam > promoters_coverage_1.1.3.txt bedtools coverage -a promoters.bed -b ../alg/1_1_4_sorted.bam > promoters_coverage_1.1.4.txt #Stress females: bedtools coverage -a promoters.bed -b ../alg/2_0_1b_sorted.bam > promoters_coverage_2.0.1b.txt bedtools coverage -a promoters.bed -b ../alg/2_0_2_sorted.bam > promoters_coverage_2.0.2.txt bedtools coverage -a promoters.bed -b ../alg/2_0_3_sorted.bam > promoters_coverage_2.0.3.txt #Stress males: bedtools coverage -a promoters.bed -b ../alg/2_1_1_sorted.bam > promoters_coverage_2.1.1.txt bedtools coverage -a promoters.bed -b ../alg/2_1_2_sorted.bam > promoters_coverage_2.1.2.txt bedtools coverage -a promoters.bed -b ../alg/2_1_3_sorted.bam > promoters_coverage_2.1.3.txt -
Parse and consolidate coverage files:
# delete the 'chr positions ' for file in ./promoters_coverage_*.txt; do awk '{$1=$2=$3=""; print substr($0, 4)}' $file > tmp.txt && mv tmp.txt $file done for file in ./promoters_coverage_*.txt; do sed 's/^ *//' $file > tmp.txt && mv tmp.txt $file done # text if they promoters_coverage_*.txt contain exactly the same set of genes (and in the same order), cut -d " " -f1 promoters_coverage_1.0.1.txt > reference_genes.txt for file in ./promoters_coverage_*.txt; do if ! cmp -s <(cut -d " " -f1 $file) reference_genes.txt; then echo "$file does not match reference!" fi done #merge the counts cut -d " " -f1 promoters_coverage_1.0.1.txt > read_counts_table.txt for file in ./promoters_coverage_*.txt; do paste read_counts_table.txt <(cut -d " " -f2 $file) > tmp && mv tmp read_counts_table.txt done #add the sample names for file in ./promoters_coverage_*.txt; do basename $file | sed 's/promoters_coverage_//;s/.txt//' done > sample_names.txt echo -n "Gene " > header.txt paste -s -d ' ' sample_names.txt >> header.txt { cat header.txt cat read_counts_table.txt } > read_counts_table_with_header.txt #Manually replace "\n" in the first line, DELETE " and "; #Delete the version of EnsembleID cp read_counts_table_with_header.txt temp awk 'BEGIN {FS=OFS="\t"} NR==1 {print; next} {gsub(/\.+[0-9]+$/, "", $1); print}' temp > read_counts_table_with_header.txt #grep "ENSMUSG00000104217.1" *.txt #Gene 1.0.1 1.0.2 1.0.3 1.1.2b 1.1.3 1.1.4 2.0.1b 2.0.2 2.0.3 2.1.1 2.1.2 2.1.3 #promoters_coverage_1.0.1.txt:"ENSMUSG00000104217.1"; 9 347 2500 0.1388000 #promoters_coverage_1.0.2.txt:"ENSMUSG00000104217.1"; 22 491 2500 0.1964000 #promoters_coverage_1.0.3.txt:"ENSMUSG00000104217.1"; 18 525 2500 0.2100000 #promoters_coverage_1.1.2b.txt:"ENSMUSG00000104217.1"; 5 255 2500 0.1020000 #promoters_coverage_1.1.3.txt:"ENSMUSG00000104217.1"; 11 315 2500 0.1260000 #promoters_coverage_1.1.4.txt:"ENSMUSG00000104217.1"; 8 300 2500 0.1200000 #promoters_coverage_2.0.1b.txt:"ENSMUSG00000104217.1"; 18 375 2500 0.1500000 #promoters_coverage_2.0.2.txt:"ENSMUSG00000104217.1"; 9 458 2500 0.1832000 #promoters_coverage_2.0.3.txt:"ENSMUSG00000104217.1"; 8 430 2500 0.1720000 #promoters_coverage_2.1.1.txt:"ENSMUSG00000104217.1"; 17 389 2500 0.1556000 #promoters_coverage_2.1.2.txt:"ENSMUSG00000104217.1"; 17 428 2500 0.1712000 #promoters_coverage_2.1.3.txt:"ENSMUSG00000104217.1"; 15 456 2500 0.1824000 #read_counts_table.txt:"ENSMUSG00000104217.1"; 9 22 18 5 11 8 18 9 8 17 17 15 #read_counts_table_with_header.txt:"ENSMUSG00000104217.1" 9 22 18 5 11 8 18 9 8 17 17 15 #reference_genes.txt:"ENSMUSG00000104217.1"; -
Load into R and format for DESeq2:
library(DESeq2) setwd("/home/jhuang/DATA/Data_Emilia_MeDIP/analysis_2023_2") # Load the data count_data <- read.table("read_counts_table_with_header.txt", header = TRUE, sep = "\t", quote = "", row.names = 1) ## Define conditions (you can replace this with your experimental design) #conditions <- c(rep("control_female", 3), rep("control_male", 3), rep("stress_female", 3), rep("stress_male", 3)) ## Create DESeq2 object #dds <- DESeqDataSetFromMatrix(countData = count_data, colData = data.frame(condition = conditions), design = ~condition) #dds <- dds[rowSums(counts(dds) > 3) > 2, ] #55401-->41415 #dds <- DESeq(dds) #rld <- rlogTransformation(dds) #dim(counts(dds)) #head(counts(dds), 10) ## This is an example using DESeq2, and it assumes count data rather than coverage. ## Proper normalization might need more manual adjustments based on the nature of MeDIP-seq data. #library(DESeq2) #dds <- DESeqDataSetFromMatrix(countData = countMatrix, colData = sampleInfo, design = ~ condition) #dds <- DESeq(dds) #normalized_counts <- counts(dds, normalized=TRUE) ## Again using DESeq2 as an example #res <- results(dds) #significant_genes <- subset(res, padj < 0.05) #------- sampleInfo <- read.csv("sampleInfo.txt", header = TRUE, stringsAsFactors = FALSE) row.names(sampleInfo) <- sampleInfo$sampleID library(DESeq2) dds <- DESeqDataSetFromMatrix( countData = count_data, colData = sampleInfo, design = ~ condition ) dds <- DESeq(dds) rld <- rlogTransformation(dds) png("pca.png", 1200, 800) plotPCA(rld, intgroup=c("condition")) dev.off() ## Again using DESeq2 as an example #res <- results(dds) #significant_genes <- subset(res, padj < 0.05) -
differential analysis
dds$condition <- relevel(dds$condition, "control_female") dds = DESeq(dds, betaPrior=FALSE) resultsNames(dds) clist <- c("stress_female_vs_control_female") dds$condition <- relevel(dds$condition, "control_male") dds = DESeq(dds, betaPrior=FALSE) resultsNames(dds) clist <- c("stress_male_vs_control_male") library(biomaRt) listEnsembl() listMarts() listDatasets(ensembl) ensembl <- useEnsembl(biomart = "ensembl", dataset = "mmusculus_gene_ensembl", version="100") datasets <- listDatasets(ensembl) listEnsemblArchives() attributes = listAttributes(ensembl) attributes[1:25,] for (i in clist) { #i<-clist[1] contrast = paste("condition", i, sep="_") res = results(dds, name=contrast) res <- res[!is.na(res$log2FoldChange),] #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("ENTREZID","EXONID","GENEBIOTYPE","GENEID","GENENAME","PROTEINDOMAINSOURCE","PROTEINID","SEQNAME","SEQSTRAND","SYMBOL","TXBIOTYPE","TXID","TXNAME","UNIPROTID")) #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "ENTREZID", "SYMBOL", "GENENAME","GENEBIOTYPE","TXBIOTYPE","SEQSTRAND","UNIPROTID")) # In the ENSEMBL-database, GENEID is ENSEMBL-ID. #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "SYMBOL", "GENEBIOTYPE")) # "ENTREZID", "TXID","TXBIOTYPE","TXSEQSTART","TXSEQEND" #geness <- geness[!duplicated(geness$GENEID), ] #using getBM replacing AnnotationDbi::select #filters = 'ensembl_gene_id' means the records should always have a valid ensembl_gene_ids. gene_ids_no_version <- gsub("\\.\\d+$", "", rownames(res)) geness <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'), filters = 'ensembl_gene_id', values = gene_ids_no_version, mart = ensembl) geness_uniq <- distinct(geness, ensembl_gene_id, .keep_all= TRUE) #merge by column by common colunmn name, in the case "GENEID" res$ENSEMBL = rownames(res) identical(rownames(res), geness_uniq$ensembl_gene_id) res_df <- as.data.frame(res) geness_res <- merge(geness_uniq, res_df, by.x="ensembl_gene_id", by.y="ENSEMBL") dim(geness_res) rownames(geness_res) <- geness_res$ensembl_gene_id geness_res$ensembl_gene_id <- NULL write.csv(as.data.frame(geness_res[order(geness_res$pvalue),]), file = paste(i, "all.txt", sep="-")) up <- subset(geness_res, padj<=0.05 & log2FoldChange>=2) down <- subset(geness_res, padj<=0.05 & log2FoldChange<=-2) write.csv(as.data.frame(up[order(up$log2FoldChange,decreasing=TRUE),]), file = paste(i, "up.txt", sep="-")) write.csv(as.data.frame(down[order(abs(down$log2FoldChange),decreasing=TRUE),]), file = paste(i, "down.txt", sep="-")) } -
clustering the genes and draw heatmap (limiting the genes with ENSEMBL by giving GO-term)
install.packages("gplots") library("gplots") # Combine the STEP 1 genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:0090399", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:0090399.csv", genes) genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:0090398", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:0090398.csv", genes) genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:2000035", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:2000035.csv", genes) genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:0017145", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:0017145.csv", genes) genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:0002761", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:0002761.csv", genes) genes <- AnnotationDbi::select(org.Mm.eg.db, keys="GO:0002244", columns=c("SYMBOL", "ENSEMBL"), keytype="GO") write.csv(file="genes_GO:0002244.csv", genes) #write.csv(file="ids_", unique(genes$ENSEMBL)) ##cut -d',' -f2 ids_ > ids ##add Gene_Id in the first line, delete the "" #GOI <- read.csv("ids")$Gene_Id GOI <- unique(genes$ENSEMBL) RNASeq.NoCellLine <- assay(rld) # Defining the custom order #column_order <- c( # "uninfected.2h_r1" #) #RNASeq.NoCellLine_reordered <- RNASeq.NoCellLine[, column_order] #head(RNASeq.NoCellLine_reordered) #clustering methods: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). pearson or spearman GOI_clean <- GOI[GOI %in% rownames(RNASeq.NoCellLine) & !is.na(GOI)] #GOI_clean <- GOI_clean[GOI_clean != "ENSMUSG00000094658"] datamat <- RNASeq.NoCellLine[GOI_clean, ] #Remove Problematic Rows whose var is 0 datamat <- datamat[!apply(datamat, 1, var) == 0, ] #datamat = RNASeq.NoCellLine[GOI, ] #rownames(datamat) <- genes$external_gene_name #DEBUG since genes has 58 records, datamat has only 48 records. write.csv(as.data.frame(datamat), file ="DEGs_heatmap_data.csv") hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete") hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete") mycl = cutree(hr, h=max(hr$height)/1.00) mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED", "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN"); mycol = mycol[as.vector(mycl)] png("DEGs_heatmap.png", width=1000, height=1010) #labRow="", heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row', scale='row',trace='none',col=bluered(75), margins=c(4,22), RowSideColors = mycol, srtCol=20, keysize=0.72, cexRow = 2, cexCol = 1.4) dev.off() # Extract rows from datamat where the row names match the identifiers in subset_1 #### cluster members ##### subset_1<-names(subset(mycl, mycl == '1')) data <- as.data.frame(datamat[rownames(datamat) %in% subset_1, ]) #212 #subset_2<-names(subset(mycl, mycl == '2')) #data <- as.data.frame(datamat[rownames(datamat) %in% subset_2, ]) #185 #subset_3<-names(subset(mycl, mycl == '3')) #data <- as.data.frame(datamat[rownames(datamat) %in% subset_3, ]) #173 # Initialize an empty data frame for the annotated data annotated_data <- data.frame() # Determine total number of genes total_genes <- length(rownames(data)) # Loop through each gene to annotate for (i in 1:total_genes) { gene <- rownames(data)[i] result <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'), filters = 'ensembl_gene_id', values = gene, mart = ensembl) # If multiple rows are returned, take the first one if (nrow(result) > 1) { result <- result[1, ] } # Check if the result is empty if (nrow(result) == 0) { result <- data.frame(ensembl_gene_id = gene, external_gene_name = NA, gene_biotype = NA, entrezgene_id = NA, chromosome_name = NA, start_position = NA, end_position = NA, strand = NA, description = NA) } # Transpose expression values expression_values <- t(data.frame(t(data[gene, ]))) #colnames(expression_values) <- colnames(data) colnames(expression_values) <- paste(colnames(data), "normalized", sep = "_") # # Combine gene information and expression data # combined_result <- cbind(result, expression_values) # Fetch raw counts for the gene from count_data raw_counts <- count_data[gene, , drop = FALSE] colnames(raw_counts) <- paste(colnames(raw_counts), "raw", sep = "_") # Combine gene information, expression data, and raw counts combined_result <- cbind(result, expression_values, raw_counts) # Append to the final dataframe annotated_data <- rbind(annotated_data, combined_result) # Print progress every 100 genes if (i %% 100 == 0) { cat(sprintf("Processed gene %d out of %d\n", i, total_genes)) } } # Save the annotated data to a new CSV file write.csv(annotated_data, "cluster1_YELLOW.csv", row.names=FALSE) #write.csv(annotated_data, "cluster2_DARKBLUE.csv", row.names=FALSE) #write.csv(annotated_data, "cluster3_DARKORANGE.csv", row.names=FALSE) #~/Tools/csv2xls-0.4/csv_to_xls.py cluster*.csv -d',' -o gene_clusters.xls -
Add pvalues of p-adjusted to the end of output using stress_female_vs_control_female-all.txt and stress_male_vs_control_male-all.txt
#python3 run.py import pandas as pd # Load the male and female stress data female_df = pd.read_csv('stress_female_vs_control_female-all.txt') male_df = pd.read_csv('stress_male_vs_control_male-all.txt') # List of files to process files_to_process = [ './raw_and_normalized_counts_GO:0090399.csv', './raw_and_normalized_counts_GO:0017145.csv', './raw_and_normalized_counts_GO:0090398.csv', './raw_and_normalized_counts_GO:0002761.csv', './raw_and_normalized_counts_GO:0002244.csv', './raw_and_normalized_counts_GO:2000035.csv' ] for filepath in files_to_process: # Load the raw data raw_df = pd.read_csv(filepath) # Extracting the pvalue and padj for female dataframe and adding them to the raw dataframe raw_df = raw_df.merge(female_df[['Unnamed: 0', 'pvalue', 'padj']], left_on='ensembl_gene_id', right_on='Unnamed: 0', how='left') raw_df = raw_df.rename(columns={'pvalue': 'pvalue_female_stress_vs_control', 'padj': 'padj_female_stress_vs_control'}) # Extracting the pvalue and padj for male dataframe and adding them to the raw dataframe raw_df = raw_df.merge(male_df[['Unnamed: 0', 'pvalue', 'padj']], left_on='ensembl_gene_id', right_on='Unnamed: 0', how='left') raw_df = raw_df.rename(columns={'pvalue': 'pvalue_male_stress_vs_control', 'padj': 'padj_male_stress_vs_control'}) # Drop the merged columns 'Unnamed: 0' from the final dataframe raw_df = raw_df.drop(columns=['Unnamed: 0_x', 'Unnamed: 0_y']) # Save the raw_df with the added pvalues and padj to a new CSV file output_filepath = filepath.replace('.csv', '_with_pvalues_and_padj.csv') raw_df.to_csv(output_filepath, index=False) #~/Tools/csv2xls-0.4/csv_to_xls.py raw_and_normalized*with_pvalues_and_padj.csv -d',' -o raw_and_normalized_with_pvalues_and_padj.xls
Unveiling Difference in Promoter regions of MeDIP data
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