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Old 08-22-2013, 05:01 AM   #1
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Posts: 264
Default Biological and technical replicates in expression analysis (DESeq)


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.

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.

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 ?

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

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