hello everybady , I'm new in rnaseq data analyse and I need to do the transcriptomic profiling between two biological conditions (wildtype and mutant) follow this plan : QC ---> indexation and mapping with STAR ----> quantication with featurecount ----> and now I would like to analyse the the featurecount.txt output in R using edgeR.
EdgeR dataset : in each featurecount output.txt I use this linux commande to :
1. cut -f1 featurecount.txt > gene_id.txt #extraire the first column called gene_id 2. cut -f7 featurecount.txt > file.txt # for each sample 3. paste gene_id file.txt(for each sample) > count.txt
I export the count.txt in using edgeR package following this script:
library(edgeR) ## Loading required package: limma library(limma) library(Glimma) library(gplots) ## Attaching package: 'gplots' library(dplyr) #library(org.Mm.eg.db #library(RColorBrewer options(width = 100) setwd("C:/Users/DIANGO/Desktop/GNF_Matrix") wt1 <- read.delim("./GWT_vs_B6.txt", stringsAsFactors = FALSE, comment.char = "#") dim(wt1) ## Create a new data object that contains just the counts. countdata <- wt1[,7, drop = FALSE] head(countdata) dim(countdata) ## Add rownames i.e. GeneIDs to data rownames(countdata) <- wt1[,1] head(countdata) # Taking a look at column names to know the sample names colnames(countdata) ##  "X.data.cephfs.punim0010.projects.Kanwal_RNASeq_Testing.seqc.test.rna.seq.work.kallisto.RNA.Test.kallisto.pseudoalignment.pseudoalignments.sorted.bam" #Renames sample name to a meaningful title colnames(countdata) <- "WILD1" head(countdata)
if there's anyone who can help me with this analysis or suggest other methods.