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Tags: R, pipeline
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. - 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.
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:
Visit the GENCODE Mouse website: https://www.gencodegenes.org/mouse/release_M25.html Download the comprehensive gene annotation GTF file for the mm10 assembly.
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
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
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