[BioC] Limma and spot-specific dye effects for an environmental microarray

[Gordon K Smyth]

Dear Ross,

Yes, you've accounted for probe-specific dye effects.

Your experiment doesn't seem to have any special complications.  Why did 
you decide to do a separate channel analysis, rather than the log-ratio 
style analysis illustrated in the limma User's Guide Case Studies of 
two-colour designs?

Best wishes
Gordon

> Date: Thu, 23 Jun 2011 14:06:36 +1000
> From: Ross Chapman <ross.chapman at ecogeonomix.com>
> To: bioconductor at r-project.org
> Subject: [BioC] Limma and spot-specific dye effects for an
> 	environmental	microarray
>
> Hi all
>
> I am trying to utilise Limma to analyses data from a two colour
> environmental microarray in an experiment that is investigating
> microbial function under contrasting vegetation types.
>
> After loading the data and performing background subtraction and
> normalisation, I have attempted to correct for spot-specific dye effects
> by including a "dye factor" in the design matrix.  My code is as
> follows:
>
>> #create design that included dye factor aswell as vegetation types ...
>> #
>> design.sc<-model.matrix(~0+factor(targets2$Target)+factor(targets2
> $channel.col))
>> colnames(design.sc)<-c("veg.3","veg.4","veg.2","veg.1","Dye")
>> design.sc
>   veg.3 veg.4 veg.2 veg.1 Dye
> 1       0       0   0     1   0
> 2       0       0   1     0   1
> 3       0       0   1     0   0
> 4       0       1   0     0   1
> 5       0       1   0     0   0
> 6       1       0   0     0   1
> 7       1       0   0     0   0
> 8       0       0   0     1   1
> 9       0       0   0     1   0
> 10      0       1   0     0   1
> 11      0       1   0     0   0
> 12      0       0   0     1   1
> 13      0       0   1     0   0
> 14      1       0   0     0   1
> 15      1       0   0     0   0
> 16      0       0   1     0   1
> attr(,"assign")
> [1] 1 1 1 1 2
> attr(,"contrasts")
> attr(,"contrasts")$`factor(targets2$Target)`
> [1] "contr.treatment"
>
> attr(,"contrasts")$`factor(targets2$channel.col)`
> [1] "contr.treatment"
>
>> # next do intraspot correlation ...
>> library(limma)
>> corfit<-intraspotCorrelation(MA,design.sc)
> Loading required package: statmod
> Warning messages:
> 1: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 2: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 3: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 4: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 5: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 6: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 7: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 8: In remlscore(y, X, Z) : reml: Max iterations exceeded
> 9: In remlscore(y, X, Z) : reml: Max iterations exceeded
>> fit<-lmscFit(MA,design.sc,correlation=corfit$consensus)
>> fit <- eBayes(fit)
>> #make contrasts ...
>> cont.matrix1 <- makeContrasts("veg.1-veg.2",levels=design.sc)
>> fit2.1 <- contrasts.fit(fit, cont.matrix1)
>> cont.matrix2 <- makeContrasts("veg.1-veg.3",levels=design.sc)
>> fit2.2 <- contrasts.fit(fit, cont.matrix2)
>> cont.matrix3 <- makeContrasts("veg.1-veg.4",levels=design.sc)
>> fit2.3 <- contrasts.fit(fit, cont.matrix3)
>> fit2.1<-eBayes(fit2.1)
>> fit2.2<-eBayes(fit2.2)
>> fit2.3<-eBayes(fit2.3)
>
> This is all a bit new for me, can someone please confirm if I have
> correctly addressed any spot*dye effects with this code.
>
> Many thanks in advance for your help.
>
> Regards
>
> Ross

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