Entering edit mode
Lina Weber
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20
@lina-weber-5123
Last seen 10.5 years ago
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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