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

[6] "high48-2.cel" "low10-1.cel"
[11] "phenoData.txt"
"low10-2.cel"
"low48-1.cel"
"low48-2.cel"
The targets file is called phenoData.txt. We see there are two arrays for each experimental
condition, giving a total of 8 arrays.
> targets <- readTargets("phenoData.txt",path=datadir,sep="",row.names="filename")
> targets
filename estrogen time.h
low10-1
low10-1.cel
absent
10
low10-2
low10-2.cel
absent
10
high10-1 high10-1.cel present
10
high10-2 high10-2.cel present
10
low48-1
low48-1.cel
absent
48
low48-2
low48-2.cel
absent
48
high48-1 high48-1.cel present
48
high48-2 high48-2.cel present
48
Now read the cel files into an AffyBatch object and normalize using the rma() function
from the affy package:
> ab <- ReadAffy(filenames=targets$filename, celfile.path=datadir)
> eset <- rma(ab)
Background correcting
Normalizing
Calculating Expression
There are many ways to construct a design matrix for this experiment. Given that we are
interested in the early and late estrogen responders, we can choose a parametrization which
includes these two contrasts.
>
>
>
>
treatments <- factor(c(1,1,2,2,3,3,4,4),labels=c("e10","E10","e48","E48"))
contrasts(treatments) <- cbind(Time=c(0,0,1,1),E10=c(0,1,0,0),E48=c(0,0,0,1))
design <- model.matrix(~treatments)
colnames(design) <- c("Intercept","Time","E10","E48")
The second coefficient picks up the effect of time in the absence of estrogen. The third
and fourth coefficients estimate the log2 -fold change for estrogen at 10 hours and 48 hours
respectively.
> fit <- lmFit(eset,design)
We are only interested in the estrogen effects, so we choose a contrast matrix which picks
these two coefficients out:
> cont.matrix <- cbind(E10=c(0,0,1,0),E48=c(0,0,0,1))
> fit2 <- contrasts.fit(fit, cont.matrix)
> fit2 <- eBayes(fit2)
We can examine which genes respond to estrogen at either time using the moderated F statistics on 2 degrees of freedom. The moderated F p-value is stored in the component
fit2$F.p.value.
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