Differential Expression Analysis In Limma For One Factor After Adjusting For A Covariate

Question

[aditi.qamra]

Hi,

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 ?

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

attr(,"assign") [1] 1 1 2 attr(,"contrasts") attr(,"contrasts")$group [1] "contr.treatment"

attr(,"contrasts")$gender [1] "contr.treatment"

Why do I not aslo see genderF as a column here ?

Thanks !

Answer 1

[Devon Ryan]

Have a read through this lab that uses limma in a very similar (2x2 factorial) design. I suspect that all of your questions will be answered by simply following through the examples there.