I am doing DE analysis on three breed groups (FS, TX, F1) with two diet conditions (C, F, where C is the normal diet and F is the additional diet). I want to find DE genes between breeds (FS vs TX, FS vs F1 and TX vs F1) both with diet effect and without diet effect. In addition, I want to find that are differentially expressed within breed due to diet (FSc vs FSf, TXc vs TXf, F1c vs F1f). I designed the experiment like this:
sampleFiles<-list.files(directory, pattern = "*_counts") sampleinfo<-read.csv("SampleTable_mRNA_all.csv", header=TRUE) row.names(sampleinfo) sample<-sampleinfo$Sample breed<-sampleinfo$Breed diet<-sampleinfo$Diet id<-sampleinfo$SampleID sampletable<-data.frame(sampleName=sample, fileName=sampleFiles,breed=breed, diet=diet, id=id) group<-factor(paste(breed, diet, sep=".")) sampletable<-cbind(sampletable, group=group) ddsMat<-DESeqDataSetFromHTSeqCount(sampleTable = sampletable, directory = directory, design=~group) ddsMatcol<-collapseReplicates(ddsMat, sampletable$id) dds<-DESeq(ddsMatcol) res<-results(dds)
Then I made comparisions like this:
For example, within-breed diet effect on TX:
dTX_Diet<-results(dds, contrast=c("group", "TX.F", "TX.C")) dTX_Diet_sig<-subset(dTX_Diet, padj< 0.05)
similarly, for identifying DE genes between FS and TX due to diet effect
dFS_TX<-results(dds, contrast=list(c("groupFS.F", "groupTX.C"), c("groupFS.C", "groupTX.F"))) dFS_TX_sig<-subset(dFS_TX, padj<0.05)
Could anybody please tell me if I would rather need to use LRT method?
In addition, I am interested to find DE genes in FS vs TX, FS vs F1 and TX vs F1 without including diet factor, in this case, how can I exclude the diet effect or the only way is to compare is that between FSc vs TXc and so on?