  Dear List,

I am sure this has been discussed before in the list, so I would be 
happy even if someone points me to the right previous discussion.

I have a Nimblegen gene expression dataset as .pairs file. Using dummy 
2nd channel reading, I have managed to normalize this dataset (vsn 
normalization). lmFit also works with the normalized dataset. However, 
when I try to perform eBayes fit, I am hitting a snag. eBayes fit 
returns a lot of error at this last stage.

I have given the steps / results from each step in this workflow. If 
someone has already faced this issue and resolved it - i would be happy 
to try it. Also, if there is a basic mistake I am making, can someone 
please let me know. Thanks. -- k.
===========================================================*
 > library(limma)
 > target=readTargets('list')
 > 
x=read.maimages(target$FileName,columns=list(R="PM",G="PM"),annotation=c("PROBE_ID","X","Y"))
Read GSM519643.pair.val
Read GSM519644.pair.val
Read GSM519645.pair.val
Read GSM519646.pair.val
Read GSM519647.pair.val
Read GSM519648.pair.val
Read GSM519649.pair.val
Read GSM519650.pair.val
Read GSM519651.pair.val
 > x$R=NULL
 > xNorm=backgroundCorrect(x,method="normexp")
 > library(arrayQualityMetrics)
Loading required package: affyPLM
Loading required package: affy
Loading required package: Biobase

Welcome to Bioconductor

   Vignettes contain introductory material. To view, type
   'openVignette()'. To cite Bioconductor, see
   'citation("Biobase")' and for packages 'citation(pkgname)'.

Loading required package: gcrma
Loading required package: preprocessCore

Attaching package: 'affyPLM'


     The following object(s) are masked from package:stats :

      resid,
      residuals,
      weights

 > xEsetNorm=new("ExpressionSet",exprs=xNorm$G)
 > library(vsn)
 > xEsetVsn=vsn(xEsetNorm)
Note:
The function 'vsn' has been superseded by 'vsn2'.
The function 'vsn' remains in the package for backward compatibility,
but for new projects, please use 'vsn2'.

vsn: 389307 x 9 matrix (1 stratum). 100% done.
 > design=model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3,))
+ )
Error in factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3, )) :
   element 9 is empty;
    the part of the args list of 'c' being evaluated was:
    (1, 1, 2, 2, 2, 3, 3, 3, )
 > design=model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3)))
 > colnames(design)=c('Ctrl','Chlr1','Chlr2')
 > design
   Ctrl Chlr1 Chlr2
1    1     0     0
2    1     0     0
3    1     0     0
4    0     1     0
5    0     1     0
6    0     1     0
7    0     0     1
8    0     0     1
9    0     0     1
attr(,"assign")
[1] 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3))`
[1] "contr.treatment"

 > xFit=lmFit(xEsetVsn,design)
 > contmat=makeContrasts(Chlr1-Ctrl,Chlr2-Ctrl,levels=design)
 > contmat
        Contrasts
Levels  Chlr1 - Ctrl Chlr2 - Ctrl
   Ctrl            -1           -1
   Chlr1            1            0
   Chlr2            0            1
 > xFit2=eBayes(xFit
xFit
 > xFit2=eBayes(xFit,contmat)
Error in log(proportion/(1 - proportion)) - log(r)/2 :
   non-conformable arrays
In addition: Warning messages:
1: In if (ntarget < 1) return(NA) :
   the condition has length > 1 and only the first element will be used
2: In if (ntarget < 1) return(NA) :
   the condition has length > 1 and only the first element will be used
3: In if (ntarget < 1) return(NA) :
   the condition has length > 1 and only the first element will be used
4: In ebayes(fit = fit, proportion = proportion, stdev.coef.lim = 
stdev.coef.lim) :
   Estimation of var.prior failed - set to default value
5: In log(proportion/(1 - proportion)) : NaNs produced
*=========================================================================

-- 
=======================================
 From Ignorance to Truth,
 From Darkness to Light,
 From Mortality to Immortality ...
=======================================


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