[BioC] edgeR: GLM - adjusting for unwanted effect?!

[Gordon K Smyth]

Dear Lina,

Responses interpolated below.

> Date: Fri, 17 Feb 2012 12:56:13 +0100
> From: Lina Weber <linamweb at googlemail.com>
> To: bioconductor at r-project.org
> Subject: [BioC] edgeR: GLM - adjusting for unwanted effect?!
>
> Dear all,
>
> I have an RNA-seq experiment where I want to test for differential
> expression in response to my applied treatment. As biological replicates I
> have two different genotypes of my clonal species, which were each exposed
> to treated and untreated conditions.
>
> The straight forward way to test for a treatment effect would therefore be:
>
> genotype <- as.factor(c("g1","g1","g2","g2"))
> treat <- as.factor(c("U","T","U","T"))
>
> design <- model.matrix(~treat)
> design
>  (Intercept) treatU
> 1           1      1
> 2           1      0
> 3           1      1
> 4           1      0
>
> ...
>
> lrt.tagd_treat <- glmLRT(D, glmfit.tagd, coef=2)
> topTags(lrt.tagd_treat)
>
> However, when I am looking at the MDS plot of my 4 samples I can see the
> the effect of the genotype is also not neglectable. Would it therefore make
> sense to include the factor genotype in the design matrix as well to adjust
> for the genotype effect in my model, e.g.:
>
> design <- model.matrix(~treat+genotype)
> design
>  (Intercept) treatU genotypeg2
> 1           1      1          0
> 2           1      0          0
> 3           1      1          1
> 4           1      0          1
>
> ...
>
> lrt.tagd_treat <- glmLRT(D, glmfit.tagd, coef=2)
> topTags(lrt.tagd_treat)
>
> 1) Is an adjustment for an "unwanted effect" possible this way in general?

Yes, this a standard technique.

> 2) Does it also make sense in my case regarding the very low level of biol.
> replication (-> 2)?

You have one degree of freedom left over for estimating the dispersion. 
It's the minimum, and it would be better to have more, but it is usable.

Best wishes
Gordon

> Thanks a lot,
> Lina
>

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