Hi

I am just trying to work out if this is the correct way of adjusting for having 2 patient groups, disease and non disease, each with different numbers of male/ females. I just want to know what genes are different between the disease and non disease, I don't care about the gender specific genes. This is my code:

design <- model.matrix(~disease+sex)

matrix <- data.matrix(countTable2)

dge <- DGEList(counts=matrix)

dge <- calcNormFactors(dge)

v <- voom(countTable2, design, plot=TRUE)

fit <- lmFit(v,design)

fit <- eBayes(fit)

top2 <- topTable(fit,coef=2,number=Inf,sort.by="P")

sum(top2$adj.P.Val<0.05)

If I include + sex when making the model matrix I get 50 DE genes, and without it I get 46.

Thanks for your help,

Chris