[BioC] DESeq(normalize using all samples?)

[Hui Zhao [guest]]

I have a file about readcount values  with eight samples(A1,A2,B1,B2,C1,C2,D1,D2),I want to know the differential genes between A and B.Normally ,I should extract sample A1,A2,B1,B2 from the file.Now I use all samples to normalize the readcounts and fit the model ,I find that I find more DE genes.I want to know if my code is true and why? In this part I find 322 genes using all samples, while I find 77 genes using specfic samples.


 -- output of sessionInfo(): 

     ###each###
     ###analysie with specific coloums###
     library('DESeq')
     x=read.delim("readcount.xls",row.names=1)
     x=round(x[,1:4])
     group=factor(c("A","A","B","B"))
     cds <- newCountDataSet(x, group)
     cds <- estimateSizeFactors(cds)
     cds <- estimateDispersions(cds)
     res <- nbinomTest(cds,'A','B')
     a<-subset(res,padj<0.05)
     dim(a)
     write.table(a[,1],"each.txt",quote=F,col.names=F,row.names=F)
 
     ###union###
     ###analysis with all coloums###
     library('DESeq')
     x=read.delim("readcount.xls",row.names=1)
     x=round(x)
     group=c("A","A","B","B","C","C","D","D")
     cds <- newCountDataSet(x, group)
     cds <- estimateSizeFactors(cds)
     cds <- estimateDispersions(cds)
     res <- nbinomTest(cds,'A','B')
     a<-subset(res,padj<0.05)
     dim(a)
    write.table(a[,1],"union.txt",quote=F,col.names=F,row.names=F)
 
    

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