I have a RNA seq data which I am trying to identify DEGs. Dataset contains:
Untreated - 2 replicates (time point 1st day)
Treated - 4 replicates (2 replicates at 3rd day & 2 replicates at 7th day )
I merged all the samples and tried to identify DEGs untreated vs treated. The 1st day sample is considered as control (untreated) for the study.
- Is it correct to merge all samples and compare. Or should I compare the two time point treated samples with untreated as 1st day vs 3rd day, 1st day vs 7th day and 3rd day vs 7th day separately.
I tried LRT test in DEseq2 to understand the changes in gene expression across time between untreated and treated. The code is given below.
sampleFiles<-grep('SRR',list.files(directory),value=TRUE) sampleCondition<-c('untreated','untreated','treated','treated','treated','treated') sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition) ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition) colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c('untreated','treated')) dds <- DESeq(ddsHTSeq, test="LRT", reduced=~1) resLRT <- results(dds) resOrdered <- resLRT[order(resLRT$padj),]
After running the code I am getting huge number of DEGs (~15000 genes) as significant DEGs after applying FDR correction cutoff <0.05.
Is there any error in mentioning the condition in the code.
Can you please tell the condition I have specified for LRT test in the code is enough to get the desired output.
Whether LRT test gives huge no of DEGs after analysis as significant DEGs.