[BioC] Link to picture: Comparison of diff. t-statistics, Limma and rowttests

[Wolfgang Huber]

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
>>
>>
> 
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