Question: Preprocessing Agilent Two-Color
0
gravatar for babak1146
2.6 years ago by
babak114620
Iran/Tehran
babak114620 wrote:

I have an Agilent 2-Color dataset (GSE23611), i am trying to normalize it with Limma package. when I normalize dataset , hclust cannot distinguish between case and control after normalization. my code is :

library(limma)
files_case <- dir(pattern="*\\.txt$")
dat <- read.maimages(files_case,source="agilent")
dat2<-normalizeWithinArrays(dat, method="loess","normexp", offset=50)
dat2 <- normalizeBetweenArrays(dat2, method="Aquantile")
dat2 <- avereps(dat2, ID=dat2$genes$ProbeName)
dat.m<-dat2$M
rownames(dat.m)<-dat2$genes$ProbeName
dat.m=na.omit(dat.m)
dat.dist<-dist(t(dat.m))
plot(hclust(dat.dist))

is it true?

gene R genome • 1.0k views
ADD COMMENTlink modified 2.6 years ago by RamRS28k • written 2.6 years ago by babak114620

Please use the formatting bar (especially the code option) to present your post better. I've done it for you this time. Formatting bar

ADD REPLYlink written 2.6 years ago by RamRS28k

Why were you expecting it to segregate cases and controls? The type of clustering that you are doing is unsupervised, i.e., using all probe expression values; thus, it should never be expected that this will segregate your dataset as you desire.

If you perform differential expression analysis between cases and controls and then perform supervised clustering using the statistically significant probes, then you should see segregation.

ADD REPLYlink written 2.6 years ago by Kevin Blighe63k
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