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lujian0311@gmail.com
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@lujian0311gmailcom-3857
Last seen 9.6 years ago
I am new to Limma and have some basic questions to ask about the
matrix
design:
(1) I have used RNA-Seq methods to get gene expression profiles from 6
patients, three of them are male, and the other three are females.
(2) For each patient, we got expresson data of the normal tissues and
cancer tissues.
I want to use Limma to analyze these RNA-Seq data and want to address
the
following questions:
(1) Differentially expressed genes in Normal and Cancer tissues in
Male.
(2) Differentially expressed genes in Normal and Cancer tissues in
Female.
(3) Whether male and females have the same set of genes that are
differentially expressed genes between normal and cancer tissues.
Thus I have the target object which is as the following:
> target
gender treatment biolrep
1 F normal 1
2 F cancer 1
3 F normal 2
4 F cancer 2
5 F normal 3
6 F cancer 3
7 M normal 4
8 M cancer 4
9 M normal 5
10 M cancer 5
11 M normal 6
12 M cancer 6
Below is the code for the limma process. I am not sure whether I
should
treat the paired samples into a block or not. Any suggestions will be
greatly appreciated.
TS<-factor(TS,levels=c("F.normal", "F.cancer", "M.normal",
"M.cancer"))
design<-model.matrix(~0+TS)
colnames(design)<-levels(TS)
corfit <- duplicateCorrelation(eset, design, ndups=1,
block=target$biolrep)
fit <- lmFit(eset, design, ndups=1, block=target$biolrep,
cor=corfit$consensus)
contrasts <-
makeContrasts(Fdif=F.normal-F.cancer,Mdif=M.normal-M.cancer,Diff=F.no
rmal-F.cancerF)-(M.normal-M.cancer),levels=design)
fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2)
results<-decideTests(fit2,lfc=log2(1.1))
vennDiagram(results)
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