how to adjust for continuous covariates in limma?

Question

[RNAseqer]

I'm fairly new to limma and just want to double check if I am doing something correctly. I have some microarray data and am looking for genes differentially expressed between states DIS and TE. I have several continuous covariates (V0, V1, V2 etc. in the phenotype data cov.tb) I am only interested in DIS/TE and would like to adjust for these covariates, excluding their effect.

I have looked into it and examined several examples but I would like a more experienced pair of eyes to confirm whether or not I have done this correctly. Is it really as simple as:

> mod1 <- model.matrix(~ 0 + mRNA_labels + V0 + V1 + V2 + V3 + V4, data=cov.tb)

Below is my entire set of commands:

> mRNA_labels <- factor(
                    c(rep('CNT', 17), rep('DIS', 21), rep('TE', 28)),
                    levels = c('CNT', 'DIS', 'TE')
                )

> mod1 <- model.matrix(~ 0 + mRNA_labels + V0 + V1 + V2 + V3 + V4, data=cov.tb)

> colnames(mod1)[1] <- "CNT"
                colnames(mod1)[2] <- "DIS"
                colnames(mod1)[3] <- "TE"
                colnames(mod1)[4] <- "V0"
                colnames(mod1)[4] <- "V1"
                colnames(mod1)[5] <- "V2"
                colnames(mod1)[6] <- "V3"
                colnames(mod1)[7] <- "V4"


> mod1
                         CNT DIS TE         V0           V1            V2           V3           V4
    VV51.CEL    1    0  0 -1.16253985 -0.112174410  0.0095394904 -0.159846427  0.124077809
    VV50.CEL    1    0  0 -0.18154195  0.148821751  0.2191634922  0.009425092 -0.089006798
    VV38.CEL    1    0  0 -6.24420703 -0.274974896  0.0397183693 -0.069572763  0.213245438
    ...

 > contrast_mod1 <- makeContrasts( 
                     DISvTE = DIS - TE,
                     levels=mod1)

> contrast_mod1
                      Contrasts
                Levels   DISvTE
                  CNT        0
                  DIS        1
                  TE        -1
                  V0         0
                  V1         0
                  V2         0
                  V3         0
                  V4         0

>fit <- lmFit(expression.dat, mod1)

>fit.cont <- contrasts.fit(fit, contrast_mod1)

>fit.eb   <- eBayes(fit.cont)

Answer 1

[i.sudbery]

Yes, it really is as simple as that as long as you expect your covariates to affect expression in a linear manner.