Question: Preprocessing Agilent Two-Color
0
gravatar for babak1146
20 months 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 • 713 views
ADD COMMENTlink modified 20 months ago by RamRS24k • written 20 months 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 20 months ago by RamRS24k

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 20 months ago by Kevin Blighe48k
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