[R] DESeq vs DESeq2 different DEGs results

[Catalina Aguilar Hurtado]

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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