[BioC] Removing genes from linear model fit advice sought

[stephen sefick]

I have done this with all of the genes in a microarray experiment
including blanks, negative controls, empties, hk genes, and spike ins,
genes of interest
1. RG <- backgroundCorrect(RG, method="normexp", offset=50)

2. MA <- maNorm(as(RG, "marrayRaw"), norm="twoD")

3. WA <- normalizeBetweenArrays(as(MA, "MAList"), method="scale")

This seems sensible to me.  Is it?

I am now thinking that I should remove everything except for the know
differentially expressed genes and the genes of interest before
fitting the linear model, contrasts, bayesian smotthing.  Is this a
sensible coarse of action?  Thanks for all of your help in advance.
kind regards,

-- 
Stephen Sefick

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K. Mullis