Dear Gordon,
I would like to thank you for pointing out the problem. This is the
first time I tried to use Limma. The main reference materials I used
is the Ch. 23 of Book Bioinformatics and Comp. Biol. solutions Using R
and BioC. and the lab notes from microarray short course @ IBC 2004.
In particular, the example I was following was the 23.10 in the book,
factorial designs where we have five chips, 2 for WT and 3 for
Mutants. In each genotype, there are unstimulated and stimulated. I
thought that resembled the experimental designs in my case (target
file):
FileName Sib Sex Treatment
anim1 1 M C
anim2 2 F C
anim3 3 M C
anim4 3 F C
anim5 1 M R
anim6 2 F R
anim7 3 M R
anim8 3 F R
Where Sib indicates sibling pairs: anim1 and anim5 are siblings and so
forth. My question is quite simple: I would like to know if there is
any difference between C and R in treatment for now. Although I might
be interested in the gender effect (and/or gender*treatment) in a
later time.
First, I read in all CEL files and normalized the chips using Limma
package and it looked quite good in diagnostic plots. Say I called
that file "eset", which is an exprSet file. I used the following
scripts to create the design matrix:
> TBS<-paste(target$Treatment, target$Sex, target$Sib, sep=".")
> TBS<-factor(TBS, levels=unique(TBS))
> design<-model.matrix(~0+TBS)
> colnames(design)<-levels(TBS)
>cont.matrix<-makeContrasts(diff=(C.M.1+C.F.2+C.M.3+C.F.3)-(R.M.1+R.F.
2+R.M.3+R.F.3))
model fitting:
>fit1<-lmFit(eset,design)
>fit2<-contrasts.fit(fit1, cont.matrix)
>fit3<-eBayes(fit2) (this is where I got the error message)
Best wishes,
Johnny
-----Original Message-----
From: Gordon Smyth [mailto:smyth@wehi.edu.au]
Sent: Wednesday, November 16, 2005 6:51 PM
To: Li at wehi.edu.au; Qinghong at wehi.edu.au; ST.LOUIS at
wehi.edu.au;
Li,Qinghong,ST.LOUIS,Molecular Biology
Cc: BioC Mailing List
Subject: [BioC] limma's eBayes error: No residual degrees of freedom
in
linear model
>[BioC] limma's eBayes error: No residual degrees of freedom in linear
model
>Li,Qinghong,ST.LOUIS,Molecular Biology Qinghong.Li at rdmo.nestle.com
>Tue Nov 15 22:09:13 CET 2005
>
>Hi BioC,
>
>I was runing eBayes and got the above error. I searched the old
archives
>of BioC, and has found similar problem poseted by Ken Ninh:
>http://files.protsuggest.org/biocond/html/4652.html
>
>I checked the summary(fit$df.residual), all zero's. But the
>fit1<-lmFit(normData, design) and fit2<-contrasts.fit(fit1,
cont.matrix)
>ran properly. I checked normData with boxplots, and they looked fine
and
>well normalized. Here is my design matrix:
> > design
> C.M.1 C.F.2 C.M.3 C.F.3 R.M.1 R.F.2 R.M.3 R.F.3 (C/R:
> control/treatment; F/M: male/female; 1,2,3 are sibling pairs)
>1 1 0 0 0 0 0 0 0
>2 0 1 0 0 0 0 0 0
>3 0 0 1 0 0 0 0 0
>4 0 0 0 1 0 0 0 0
>5 0 0 0 0 1 0 0 0
>6 0 0 0 0 0 1 0 0
>7 0 0 0 0 0 0 1 0
>8 0 0 0 0 0 0 0 1
>attr(,"assign")
>[1] 1 1 1 1 1 1 1 1
>attr(,"contrasts")
>attr(,"contrasts")$TBS
>[1] "contr.treatment"
>
>contrast matrix
>
> > cont.matrix
> Diff
>C.M.1 -1
>C.F.2 -1
>C.M.3 -1
>C.F.3 -1
>R.M.1 1
>R.F.2 1
>R.M.3 1
>R.F.3 1
>
>What could be the possible reasons for the error and how to fix that?
>
>Thanks
>Johnny
Dear Johnny,
I have to tell you that what you're doing, i.e., the design matrix
you've
created, is not very sensible statistically. Hence the non-useful
results
you are getting from limma. Here are some steps that you can take to
do
something about it:
1. Consult someone with statistical experience at your organization
who can
tell you about replication and degrees of freedom for error.
2. To get meaningful help from this list, you need to explain a little
more
about your experiment. In particular you need to explain what you are
hoping to learn scientifically from your data and what comparisons are
of
interest to you.
3. Explain what documentation you have read and what examples you are
attempting to follow here. That would help us understand what you need
to
know, and may also help us to improve the documentation.
Best wishes
Gordon
