Hi list,

I am interested in testing Genetic x Environment interaction for RNA-Seq
data. However, if I understood correctly, DeSeq do not test for
interaction, right?

My experimental design consists in comparing expression of two different
populations, two replicates per population (Pop A1, Pop A2 vs. Pop B1 and
Pop B2), at different temperatures (T1 and T2). The next table represents
my data frame and my procedure:





myData:

A1.T1                  A2.T1                  B1.T1
B2.T1
A1.T2                  A2.T2                 B1.T2                 B2.T2

Gene1      count                  count                  count
count                  count                  count
count
count

Gene2      count                  count                  count
count                  count                  count
count
count

…

Gene n     count                  count                  count
count                  count                  count
count
count





Desing = data.frame (treatment = ("T1"," T1"," T1"," T1"," T2"," T2","
T2"," T2"), block = c("A1","A2","B1","B2","A1","A2","B1","B2"))

cds = newCountDataSet(myData, desing)

cds = estimateSizeFactors(cds)

cds = estimateVarianceFunctions(cds, method = "pooled")

fit0 = nbinomFitGLM(cds, count ~ block)

fit1 = nbinomFitGLM(cds, count ~ block + treatment)

fit2 = nbinomFitGLM(cds, count ~ block + treatment + block:treatment)

pvals2 = nbinomGLMTest (fit2, fit1)

padj2 = p.adjust (pvals2, method = "BH")

pvals = nbinomGLMTest (fit1, fit0)

padj = p.adjust (pvals, method = "BH")

myData = data.frame (myData, pvals, padj)



May I use the same cds to test fit0, fit1 and fit2?

If understand ok, this is not correct since I need to estimate different
SizeFactor and VarianceFuntions for each GLM, wright?



If I am wright, is then there anyway to test for Block, treatments and
Block:Treatment with DeSeq?


Thank you very much for your help.



Miguel

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