Biological and technical replicates in expression analysis (DESeq)

NicoBxl · 08-22-2013, 05:01 AM

Hi,

I've to analyze several RNA-Seq samples. I've samples from several runs, unstraned and straned, and several samples sequenced multiple times ( using different library kit ). I used htseq-count to have the read counts and want now to use DESeq to check for differential expression. So I've biological replicates and technical replicates (same sample sequences several times using a different lib kit. Is that correct ?).

So I did a design matrix. In my example, A.1 means sample A, sequencing 1. A.2 : sample A, sequencing 2,... So A is sequenced two times (One unstranded, one stranded), B three times (One unstranded, two stranded), C one time (one unstraned) and D one time (one stranded). ReplicateGroup is used to put together technical replicates.

Code:

designTable : 
Sample      Condition  Stranded   ReplicateGroup
A.1         Ctrl          No             A
B.1         Treated       No             B
C.1         Treated       No             C
A.2         Ctrl          Yes            A
B.2         Treated       Yes            B
B.3         Treated       Yes            B
D.1         Treated       Yes            D

After that I use DESeq. countTable is the read count matrix.

Code:

cdsFull = newCountDataSet( countTable, designTable )
cdsFull = estimateSizeFactors( cdsFull )
cdsFull = estimateDispersions( cdsFull )
But now I don't know how to fit a model o

n "condition" "stranded" and "replicateGroup".

like that ?

Code:

fit1 = fitNbinomGLMs( cdsFull, count ~ Condition + Stranded + ReplicateGroup )
fit0 = fitNbinomGLMs( cdsFull, count ~ Condition )
pvalsGLM = nbinomGLMTest( fit1, fit0 )
padjGLM = p.adjust( pvalsGLM, method="BH" )

Is it the good way to analyze technical replicated. I read that I have to merge them together.. but I don't think it's a good idea due to the fact that I use different library kits. So I'm stuck...

Thanks a lot in advance

N.

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08-22-2013, 05:01 AM	#1
NicoBxl not just another member Location: Belgium Join Date: Aug 2010 Posts: 264	Biological and technical replicates in expression analysis (DESeq) Hi, I've to analyze several RNA-Seq samples. I've samples from several runs, unstraned and straned, and several samples sequenced multiple times ( using different library kit ). I used htseq-count to have the read counts and want now to use DESeq to check for differential expression. So I've biological replicates and technical replicates (same sample sequences several times using a different lib kit. Is that correct ?). So I did a design matrix. In my example, A.1 means sample A, sequencing 1. A.2 : sample A, sequencing 2,... So A is sequenced two times (One unstranded, one stranded), B three times (One unstranded, two stranded), C one time (one unstraned) and D one time (one stranded). ReplicateGroup is used to put together technical replicates. Code: designTable : Sample Condition Stranded ReplicateGroup A.1 Ctrl No A B.1 Treated No B C.1 Treated No C A.2 Ctrl Yes A B.2 Treated Yes B B.3 Treated Yes B D.1 Treated Yes D After that I use DESeq. countTable is the read count matrix. Code: cdsFull = newCountDataSet( countTable, designTable ) cdsFull = estimateSizeFactors( cdsFull ) cdsFull = estimateDispersions( cdsFull ) But now I don't know how to fit a model o n "condition" "stranded" and "replicateGroup". like that ? Code: fit1 = fitNbinomGLMs( cdsFull, count ~ Condition + Stranded + ReplicateGroup ) fit0 = fitNbinomGLMs( cdsFull, count ~ Condition ) pvalsGLM = nbinomGLMTest( fit1, fit0 ) padjGLM = p.adjust( pvalsGLM, method="BH" ) Is it the good way to analyze technical replicated. I read that I have to merge them together.. but I don't think it's a good idea due to the fact that I use different library kits. So I'm stuck... Thanks a lot in advance N.