Entering edit mode

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
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