Dear Boel How does the "pairs" plot look like for the matrix with rows = genes, columns = three different ways of computing t? Can you single out the data for one particular gene where you get a big difference (e.g. where ordinary.t is so large) and trace back how the computations in lmFit produce that result? Best wishes Wolfgang ------------------------------------------------------------------ Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber 25/07/2008 12:19 Boel Brynedal scripsit > Dear List, > > Thank you (Wolfgang and Paolo) for telling me the attachment did not get > through. This is a link to the picture: > http://picasaweb.google.se/Boelbubblan/Statistics/photo > > Cheers, > Boel > > --~*~**~***~*~***~**~*~-- > Boel Brynedal, MSc, PhD student > Karolinska Institutet > Department of Clinical neuroscience > > > ----- Original Message ----- > From: Boel Brynedal <boel.brynedal at="" ki.se=""> > Date: Friday, July 25, 2008 9:33 am > Subject: Comparison of diff. t-statistics, Limma and rowttests > To: bioconductor at stat.math.ethz.ch > >> Dear List, >> >> I have affy hgu133plus2 arrays from individuals with disease, in two >> different stages of the disease. I've earlier used rowttests and FDR >> correction. Now I was playing around with limma to see what I could do >> (added different covariates etc) but also investigated the most simple >> setting, comparing the two different stages directly using Limma. The >> first thing that struck me was that limma "finds" only half the amount >> of significantly diff expressed genes. So I started to look at the >> t-statistics from limma. Then I stumbled across this: when I do a >> qq-plot of the ordinary t-statistics they are far from normally >> distributed, and actually totally strange. See attached plot comparing >> the ordinary t, the moderate t (both from Limma) as well as t- >> statisticsfrom rowttests ("Diff_tStatistics_Limma.jpg"). >> >> Am I doing something completely wrong? The assumption of equal >> variancetaken using ordinary t could not create this, could it? >> Please help me >> figure out what's wrong here, I'm hoping I've done some stupid >> mistake.What else could explain this? Thank you. >> >> Best wishes, >> Boel >> >> My code and sessionInfo: >> >> # eset is a filtered, gcrma normalized ExpressionSet with ~10 000 >> probesets, 24 arrays. >> library(limma) >> library(Biobase) >> library(genefilter) >> specific<-factor(c(rep("stageA",10),rep("stageB",14)), >> levels=c("stageB","stageA")) >> design<-model.matrix(~specific) >> fit<-lmFit(eset,design) >> Fit<-eBayes(fit) >> >> ordinary.t <- fit3$coef / fit3$stdev.unscaled / fit3$sigma >> moderate.t<-Fit$t[,2] >> rowttests.t<-rowttests(eset,fac=specific) >> >> par(mfrow=c(1,3)) >> qqnorm(ordinary.t,main="fit ordinary.t") >> qqnorm(moderate.t, main=" Fit moderate.t") >> qqnorm(rowttests.t[,1], main= "rowttests.t") >> dev2bitmap("Diff_tStatistics_Limma.jpg",type="jpeg", height = 5, >> width = >> 15, res = 75) >> >>> sessionInfo() >> R version 2.7.1 (2008-06-23) >> x86_64-unknown-linux-gnu >> >> locale: >> ... >> >> attached base packages: >> [1] splines tools stats graphics grDevices utils >> datasets[8] methods base >> >> other attached packages: >> [1] genefilter_1.20.0 survival_2.34-1 Biobase_2.0.1 limma_2.14.5 >> >> loaded via a namespace (and not attached): >> [1] annotate_1.18.0 AnnotationDbi_1.2.2 DBI_0.2-4 >> [4] RSQLite_0.6-9 >> >> --~*~**~***~*~***~**~*~-- >> Boel Brynedal, MSc, PhD student >> Karolinska Institutet >> Department of Clinical neuroscience >> >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor