Question: DESeq vs DESeq2 have different DEGs results

0

Catalina Aguilar Hurtado •

**50**wrote:Hi I want to compare DESeq vs DESeq2 and I am getting different number
of
DEGs which I will expect to be normal. But 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\'92t 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\'92t this two methods output similar results? Because at the
moment I could interpret my results with 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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modified 5.0 years ago
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5.0 years ago by
Catalina Aguilar Hurtado •

**50**