[BioC] Link to picture: Comparison of diff. t-statistics, Limma and rowttests
Boel Brynedal
Boel.Brynedal at ki.se
Tue Aug 12 10:03:27 CEST 2008
Dear List,
I suddenly understood that had happened and just thought I would let you
know. ordinary.t has two columns;
> ordinary.t[1:3,]
(Intercept) specificstageA
117_at 34.92382 1.7682627
1552264_a_at 39.52028 -1.5384353
1552277_a_at 23.21122 -1.6098082
so I made the mistake to plot both of these at the same time. Sorry to
take up your time, and thanks for your replys!
Best,
Boel
----- Original Message -----
From: Wolfgang Huber <huber at ebi.ac.uk>
Date: Friday, July 25, 2008 5:11 pm
Subject: Re: [BioC] Link to picture: Comparison of diff. t-statistics,
Limma and rowttests
To: Boel Brynedal <Boel.Brynedal at ki.se>
Cc: bioconductor at stat.math.ethz.ch
>
> 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
> bigdifference (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
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