Download limma: Linear Models for Microarray Data User's Guide

This is an example of a single comparison experiment using a common reference. The fact
that the comparison is made by way of a common reference rather than directly as for the
swirl experiment makes this, for each gene, a two-sample rather than a single-sample setup.
> load("ApoAI.RData")
> objects()
[1] "RG"
> names(RG)
[1] "R" "G" "Rb" "Gb" "printer" "genes" "targets"
> RG$targets
FileName Cy3
Cy5
c1 a1koc1.spot Pool C57BL/6
c2 a1koc2.spot Pool C57BL/6
c3 a1koc3.spot Pool C57BL/6
c4 a1koc4.spot Pool C57BL/6
c5 a1koc5.spot Pool C57BL/6
c6 a1koc6.spot Pool C57BL/6
c7 a1koc7.spot Pool C57BL/6
c8 a1koc8.spot Pool C57BL/6
k1 a1kok1.spot Pool ApoAI-/k2 a1kok2.spot Pool ApoAI-/k3 a1kok3.spot Pool ApoAI-/k4 a1kok4.spot Pool ApoAI-/k5 a1kok5.spot Pool ApoAI-/k6 a1kok6.spot Pool ApoAI-/k7 a1kok7.spot Pool ApoAI-/k8 a1kok8.spot Pool ApoAI-/> MA <- normalizeWithinArrays(RG)
> cols <- MA$targets$Cy5
> cols[cols=="C57BL/6"] <- "blue"
> cols[cols=="ApoAI-/-"] <- "yellow"
> boxplot(MA$M~col(MA$M),names=rownames(MA$targets),col=cols,xlab="Mouse",ylab="M-values")
Since the common reference here is a pool of the control mice, we expect to see more differences
from the pool for the knock-out mice than for the control mice. In terms of the above plot,
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