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On Mon, May 5, 2014 at 9:42 AM, Catalina Aguilar Hurtado
<catagui@gmail.com>wrote:

> Hi,
>
> I want to compare DESeq vs DESeq2 and I am getting different number of DEGs
> which I will expect to be normal. However, when I compare the 149 genes ID
> that I get with DESeq with the 869 from DESeq2 there are only ~10 genes
> that are in common which I don’t understand  (using FDR <0.05 for both). I
> want to block the Subject effect for which I am including the reduced
> formula of ~1.
>
> Shouldn’t these two methods output similar results?  Because at the moment
> I could interpret my results in different ways.
>
> Thanks for your help,
>
> Catalina
>
>
> This the DESeq script that I am using:
>
>
> DESeq
>
> library(DESeq)
>
> co=as.matrix(read.table("2014_04_01_6h_LP.csv",header=T, sep=",",
> row.names=1))
>
>
> Subject=c(1,2,3,4,5,1,2,4,5)
>
> Treatment=c(rep("co",5),rep("c2",4))
> a.con=cbind(Subject,Treatment)
>
> cds=newCountDataSet(co,a.con)
>
>
> cds <- estimateSizeFactors( cds)
>
> cds <- estimateDispersions(cds,method="pooled-CR",
> modelFormula=count~Subject+Treatment)
>
>
> #filtering
>
> rs = rowSums ( counts ( cds ))
> theta = 0.2
> use = (rs > quantile(rs, probs=theta))
> table(use)
> cdsFilt= cds[ use, ]
>
>
>
> fit0 <- fitNbinomGLMs (cdsFilt, count~1)
>
> fit1 <- fitNbinomGLMs (cdsFilt, count~Treatment)
>
> pvals <- nbinomGLMTest (fit1, fit0)
>
>
> padj <- p.adjust( pvals, method="BH" )
>
> padj <- data.frame(padj)
>
> row.names(padj)=row.names(cdsFilt)
>
> padj_fil <- subset (padj,padj <0.05 )
>
> dim (padj_fil)
>
> [1] 149   1
>
>
> ——————
>
> library ("DESeq2")
>
> countdata=as.matrix(read.table("2014_04_01_6h_LP.csv",header=T, sep=",",
> row.names=1))
>
> coldata= read.table ("targets.csv", header = T, sep=",",row.names=1)
>
> coldata
>
> >Subject Treatment
> >F1       1        co
> >F2       2        co
> >F3       3        co
> >F4       4        co
> >F5       5        co
> >H1       1        c2
> >H2       2        c2
> >H4       4        c2
> >H5       5        c2
>
> dds <- DESeqDataSetFromMatrix(
>   countData = countdata,
>   colData = coldata,
>   design = ~ Subject + Treatment)
> dds
>
> dds$Treatment <- relevel (dds$Treatment, "co")
>
>
> dds <- estimateSizeFactors( dds)
>
> dds <- estimateDispersions(dds)
>
>
> rs = rowSums ( counts ( dds ))
> theta = 0.2
> use = (rs > quantile(rs, probs=theta))
> table(use)
> ddsFilt= dds[ use, ]
>
>
> dds <- nbinomLRT(ddsFilt, full = design(dds), reduced = ~ 1)
>
> resLRT <- results(dds)
>
> sum( resLRT$padj < 0.05, na.rm=TRUE )
>
> #[1] 869
>
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>
>
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