Question: classifyTestsP vs topTable
0
gravatar for Tarca Adi Laurentiu
14.5 years ago by
Tarca Adi Laurentiu100 wrote:

Hi everybody,

I use limma to analyze a two-color microarrays data set. Using topTable I find say 15 genes with "holm" adjusted p-values less than a given threshold pt=0.05, but if I use classifyTestsP (specifying the same threshold and adjustment method) I obtain much more than 15. Is there any explanation for this? Here is the code, supposing that the normalized data is available as an object called MA.

design <-cbind("L-H1"=c(0,1,-1,0,1,0,-1,0),"L-H7"=c(-1,0,0,-1,0,1,0,1))
cor <- duplicateCorrelation(MA,design,ndups=2,spacing=1)
fit <- lmFit(MA,design,ndups=2,spacing=1,correlation=cor$consensus.cor
relation)
fit <- eBayes(fit)
tt1<-topTable(fit,n=500,adjust.method="holm")
tt<-tt1[tt1$P.Value<0.05,]
dim(tt)
[1] 15 11

So there are 15 genes with p.values less than 0.05. Now using classifyTestsP:

x<-classifyTestsP(fit,p.value=0.05, method="holm")
sum(abs(x[,1])>0)
[1] 235

there appears to be 235.

What am I doing wrong?

Thanks a lot, Laurentiu Tarca

limma • 351 views
ADD COMMENTlink modified 4 weeks ago by Gordon Smyth36k • written 14.5 years ago by Tarca Adi Laurentiu100
Answer: classifyTestsP vs topTable
0
gravatar for Gordon Smyth
4 weeks ago by
Gordon Smyth36k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth36k wrote:

Please use decideTests instead of classifyTestsP. The latter does not adjust for multiple testing across genes and is not intended to be called directly by users.

ADD COMMENTlink written 4 weeks ago by Gordon Smyth36k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 291 users visited in the last hour