[BioC] limma's eBayes error: No residual degrees of freedom in linear model

[Li,Qinghong,ST.LOUIS,Molecular Biology]

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 at 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