[BioC] Interaction categorical/continuous variable DESeq2

[Hugo Varet]

Dear list, dear Mike Love,

I am using DESeq2 to model counts from an unusual type of experiment and 
I have a question about the strategy I employed. The experiment 
consisted in sequencing 33 samples for which we have the following 
information:
  - group (16 samples from group A and 17 from group B)
  - a continuous variable X almost uniform (variable of interest)

I have to add the group to the design formula because I know it has a 
strong effect on the counts. Then, as my goal is to detect genes which 
vary with the continous variable X in the same way within both groups A 
and B, I want to exclude genes for which there is an interaction between 
group and X. The design is thus ~ group + X + group:X and I used the 
following lines to test the interaction:

dds <- DESeqDataSetFromMatrix(countData=counts, colData=target, design = 
~ group + X + group:X)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
res <- results(dds, name="groupB.X")
sum(res$padj<=0.05, na.rm=TRUE)
hist(res$padj)

As I found no significant interaction (the minimum adjusted p-value is 
about 0.6), I decided to remove the interaction term from the design and 
to use ~ group + X. I can then test for the coefficients of X.

If I do not detect any significant interaction, I think it is due to a 
lack of power. So, can I use the additive model ~ group + X even if it 
will not be correct for genes which actually have an interaction?

Many thanks in advance,

Hugo

PS: I am using R 3.1.1 and DESeq2 1.4.5