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 !

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