I am attempting to analyze single-channel Agilent micro array data from two different groups. I have numerous covariates for each individual within these groups and have specified categorical covariates using the factor() method.

Sex <- targets[,"Sex"] #Note: targets is the target file containing array filenames and covariates.
Levels <-c("1", "2")
Sex <- factor(Sex, levels=Levels)

In order to build my model matrix with Class (the thing I am ultimately interested in comparing), batch, sex, and pH... I use the following command.

design <-model.matrix(~Class + Batch + Sex + pH)

And if I view this model matrix...

   (Intercept) ClassControl Batch2 Sex2   pH
1            1            0      1    0 6.29
2            1            0      1    1 6.44
3            1            1      1    1 6.20

Question 1: Why does the model matrix statement add "control" to the first variable and "2" to the two... but not the last?

To fit the model, I use the following code...

fit <- lmFit(y0, design) #Note: y0 has been normalized and background corrected
fit <-eBayes(fit, trend=TRUE)
results <- topTable(fit, n=Inf, coef=2)
write.table(results, 'results.txt', sep='\t', col.names=TRUE, row.names=FALSE)

Question 2: From what I read, the contrast statement is made by topTable() and not the model matrix. Thus, by specifying coef=2, I am telling limma that I'm interested in comparing the 2nd column, which corresponds to "Class." But, because the other covariates were including in the model statement, the model is adjusting for those covariates. Is that correct?

Question 3: Is there any way to view the fit (AIC/BIC) for the topTable() method. Essentially, I have other covariates and would like some objective measure on how the model is effected when they are added to the model.

I appreciate your help/feedback.

Answer 1. Class, Batch and Sex are all factors with two levels, whereas pH is a numeric covariate. When the factors enter the design matrix, the first level of the factor is absorbed into the intercept and you get a design column for the second and subsequent levels of the factor. The column is called XXYY where XX is the name of the factor and YY is the level. For example, Sex has levels 1 and 2, so the column for the second level is called "Sex2". Continuous covariates don't have levels -- for them the column just has the same name as the variable.

Answer 2. Yes.

Answer 3. The model is not fitted by AIC or BIC, so these measures are not meaningful here. To judge whether other covariates are needed, just try putting them in the model and see if they generate DE genes.