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

Thanks a lot,
Lina

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