[BioC] Comparison of diff. t-statistics, Limma and rowttests
Boel Brynedal
Boel.Brynedal at ki.se
Fri Jul 25 09:33:26 CEST 2008
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-statistics
from rowttests ("Diff_tStatistics_Limma.jpg").
Am I doing something completely wrong? The assumption of equal variance
taken 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 probe
sets, 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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