Dear list,

I am triying to use limma to compare 6 different treatments.- According to
how the data have been generated, appart from the treatment effect, there
are two other clear effects 1) due to the cell lines where the treatments
have been applied and 2) due to the genetic relationship between the animals
where the cells were extracted.- Well, I would like to make a comparison
between treatments taking into account the variability that these tow
additional sistematic effects introduce in the data.-  The model equation
would be:

Y = treatment + cell +  sib + error .-

I am constructing the design matrices manually as follows:

sibEFF=factor(c("mr1","mr1","mr1","mr2","mr2","mr2","mr3","mr3","mr3",
"mr1","mr1","mr1","mr2","mr2","mr2","mr3","mr3","mr3"),
levels=c("mr1","mr2","mr3"))

claEFF=factor(c("c1","c1","c1","c2","c2","c2","c3","c3","c3","c4","c4","c4",
"c5","c5","c5","c6","c6","c6"), levels=c("c1","c2","c3","c4","c5","c6"))


ttoEFF=factor(c("t1","t2","t3","t1","t2","t3","t1","t2","t3","t4",
"t5","t6","t4","t5","t6","t4","t5","t6"),
levels=c("t1","t2","t3","t4","t5","t6"))

design=model.matrix(~   - 1 + ttoEFF + claEFF + sibEFF)
CM= makeContrasts(t1-t2,levels=ttoEFF)
fit=lmFit(eset,design)

I do not know how the parameters of the model are estimated. I guess that
with this model equation,  the X’X matrix is not singular and some
coefficients of the cell and sib effects can not be estimated.-

>From here I get an error message when I apply fit2=contrasts.fit(fit,CM). I
have to be doing something wrong but I do not know what it is.

I was thinking to fit first the linear model

Y = cell +  sib + error
and from here I think I could use limma with the estimated error terms to
get the DE genes due to the treatment effect. Could be this a good strategy?


I would appreciate very much if someone could give me any advice.

Other think that I would like to know is if it s possible to check the
quality of the limma models (some residual analysis, QQ-plots, BIC ,…).

Thanks for any suggestion.-

Pedro.-







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