I am trying to perform differential expression analysis on a sample set that looks like this:
DataFrame with 33 rows and 3 columns ID Status Sex <character> <factor> <factor> BP10 BP10 Control male BP17 BP17 Control female BP18 BP18 Exp male BP19 BP19 Control female BP1 BP1 Input male ... ... ... ... v2_BP5 v2_BP5 Input female v2_BP6 v2_BP6 Exp female v2_BP7 v2_BP7 Exp female v2_BP8 v2_BP8 Exp female
I want to do differential expression analysis between Control and Experimental with DESeq2 (which I can do) but I want to essentially 'normalize' the samples to the input first, before doing DESeq. Is this possible? I haven't been successful thus far.
Here's what my DESeq code usually looks like if I was just going to compare experimental to control without taking input into consideration:
dds<-DESeqDataSet(se=se,design=~Status) dds<-DESeq(object=dds) dds<-estimateSizeFactors(dds) (res<-results(object=dds, alpha=0.05, lfcThreshold=1.5, pAdjustMethod='BH', contrast=c('Status','Exp','Control'))) summary(res)
Thanks in advance for any advice!