[BioC] Differential expression analysis in Limma for one factor after adjusting for a covariate
James W. MacDonald
jmacdon at uw.edu
Fri Aug 30 15:34:37 CEST 2013
On Friday, August 30, 2013 5:49:51 AM, QAMRA Aditi (GIS) wrote:
> I have an expression dataset for both normal and diseased patients as well as their gender information. What I want to know is to test for difference in expression of males and females after having adjusted for differences between a normal and diseased tissue type (group ) using Limma rather than anova function in R,
> I have 2 questions -
> 1. Does Limma allow inclusion of covariates ? How do I first adjust the expression dataset to remove differences because of the sample being a diseased sample and then understand the true difference between the exp of male and female in Limma. What I have been able to do uptil now is difference between males/females and normals/diseased. Would (Male.Diseased-Male.Normal)-(Female.Diseased-Female.Normal) (which is basically an interaction term) would give me this ?
Any time you fit a model with various coefficients included, you are
automatically adjusting for those coefficients. In other words, if you
fit a model with sex and treatment and then compute the contrast
between male and female, you are doing so after adjusting for treatment.
But your question isn't that clear, so I don't know if that answers it.
The interaction term gives you those genes that react differently to
the treatment in males as compared to females. This is different from
finding genes that are different in males vs females after adjusting
for treatment, but again it isn't totally clear to me what you are
> 2. I was trying include both gender and group information as factors - but when Im trying to build the model matrix -
> design <- model.matrix(~0+gender+group)
> where both gender and group are factors - i get the following layout of the design matrix -
> groupnormal groupdiseased genderM
> 1 1 0 0
> 2 1 0 1
>  1 1 2
>  "contr.treatment"
>  "contr.treatment"
> Why do I not aslo see genderF as a column here ?
Because that is the way R sets up the model matrix. The genderM
coefficient is computing the difference between males and females, so
if you want to test for sex differences you would simply test that this
coefficient is different from zero.
But this is something that Gordon has been pointing out for years; the
conventional coefficients that you get from model.matrix() may not be
the most useful in the context of a microarray experiment. You could
instead do something like
groupGend <- factor(paste(group, gender, sep = "_"))
design <- model.matrix(~0+groupGend)
and then your coefficients will be something directly interpretable,
and easier to understand (e.g., you will have four coefficients,
male_normal, male_diseased, female_normal, female_diseased, and then
you can make more directed comparisons).
> Thanks !
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James W. MacDonald, M.S.
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
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