[BioC] Interaction categorical/continuous variable DESeq2

Wolfgang Huber whuber at embl.de
Mon Sep 15 19:54:34 CEST 2014


Cher Hugo

sorry if I missed something, but why not fit the model with interactions and test for the coefficient of the ‘X' main effect?
(see arguments ‘name’, ‘contrast’ of the ‘results’ function).

What you propose below seems not wrong, but perhaps unnecessarily complicated.

	Best wishes
	Wolfgang

Il giorno 15 Sep 2014, alle ore 15:41, Hugo Varet <hugo.varet at pasteur.fr> ha scritto:

> 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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