RNA_Seq Data Analyse

Question

[Diango]

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)

## [1] "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.

[Kevin Blighe]

Please edit your question to re-format the code. Essentially, highlight your code chunks and then click on the 101 010 button. Thanks.

[Diango]

My questions is juste focus to how analysis featurecount output file with edgeR or DESeq2 R package. thk @kevin for your clarification

[ATpoint]

What is unclear after reading the manual from edgeR? It involves all code for a standard analysis.

Answer 1

[brianj.park]

After you imported your count table into R, you have to create a DGEList object that contains your counts, gene IDs, and sample group info needed for differential expression. Follow this vigenette and it should be straightforward.

[Diango]

thank you very much brian j.park I'm doing great with your link