[BioC] LIMMA: testing for batch effects

[Adaikalavan Ramasamy]

Dear all,

We have 63 arrays that are either diseased or normal. We wish to select
genes adjusting for two covariates : gender (male/female) and
experimental batch (one/two/three).

>From biological knowledge, we expect the batch effect to be significant
but we wish to quantify it numerically. Here is a way that we tried and
the problems we faced. We searched the archives without much success. 


 library(limma)  # version 2.3.3
 dd  <- model.matrix( ~ disease + gender + batch, cl )
 head( dd ) 
       (Intercept) diseasenormal gendermale batchtwo batchthree
 2405            1             1          0        0          0
 2408            1             1          0        0          0
 2410            1             1          0        0          0
 GER15           1             0          0        0          0
 GER20           1             0          0        0          1
 GER22           1             0          0        0          0

 fit  <- lmFit(mds.rma, dd)


1) Is the following the correct way of setting up contrasts to find the
three pairwise comparison between batches ?

 contr.m <- cbind( Batch2minus1=c(0,0,0,1,0), Batch2minus1=c(0,0,0,0,1),
                   Batch3minus2=c(0,0,0,-1,1) )


2) From reading the LIMMA user guide, we think decideTests() could be
potentially useful. 

 fit2 <- eBayes( contrasts.fit( fit, contr.m ) )
 a <- decideTests( fit2, method="global" )
 summary(a)
    Batch2minus1 Batch2minus1 Batch3minus2
 -1        15151        13838         4919
  0        26574        30561        46703
  1        12950        10276         3053

Can we say that somewhere between 8000 - 27000 genes are affected by at
least one batch. Or is there a nicer/proper way of explaining this to a
biologist.


3) Ideally we would like to use method="nestedF" as suggested but we get
the following error message. Can anyone explain what might possibly be
going wrong.

 b <- decideTests( fit2, method="nestedF" )
 Error in if (crossprod(crossprod(Q, x)) > qF[i]) { : 
  	missing value where TRUE/FALSE needed


Any hints will be much appreciated. 

Regards, 

-- 
Adaikalavan Ramasamy                    ramasamy at cancer.org.uk
Centre for Statistics in Medicine       http://www.csm-oxford.org/
Wolfson College Annexe                  http://www.stats.ox.ac.uk/~ramasamy/
Linton Road                             Tel : 01865 284 408
Oxford OX2 6UD                          Fax : 01865 284 424