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Chapter 12
Separate Channel Analysis of
Two-Color Data
Separate channel analysis is a way to analyse two-color data in terms of the individual channel
intensities [41]. In effect, separate channel analysis converts a two-color experiment into a
single channel experiment with twice as many arrays but with a technical pairing between the
two channels that originated from the same array.
Consider an experiment comparing young and old animals for both both wild-type and
mutant genotypes.
FileName
Cy3
Cy5
File1 wt.young
wt.old
File2
wt.old wt.young
File3 mu.young
mu.old
File4
mu.old mu.young
Each of the arrays in this experiment makes a direct comparison between young and old RNA
targets. There are no arrays which compare wild-type and mutant animals. This is an example
of an unconnected design in that there are no arrays linking the wild-type and mutant targets.
It is not possible to make comparisons between wild-type and mutant animals on the basis of
log-ratios alone. So to do this it is necessary to analyze the red and green channels intensities
separately, i.e., to analyze log-intensities instead of log-ratios. It is possible to do this using
a mixed model representation which treats each spot as a randomized block [48, 37]. Limma
implements mixed model methods for separate channel analysis which make use of shrinkage
methods to ensure stable and reliable inference with small numbers of arrays [37]. Limma also
provides between-array normalization to prepare for separate channel analysis, for example
> MA <- normalizeBetweenArrays(MA, method="Aquantile")
scales the intensities so that A-values have the same distribution across arrays.
The first step in the differential expression analysis is to convert the targets frame to be
channel rather than array orientated.
> targets2 <- targetsA2C(targets)
> targets2
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