Dear Gordon

Thank you for your prompt and helpful reply.

I chose the 'two channel' analysis instead of the log ratios for a
couple of reasons.

Firstly, the two channel analysis was more intuitive and easier for me
to understand given my ecological  background.

Secondly, in the experiment I have 4 samples (veg.1 to veg.4) hybridised
using two dyes (cye3 and5) across 8 arrays.  Veg.1 is paired with all
other samples, but on only one array for Veg.2 and Veg.3, I was
uncertain as to whether this design was sufficiently connected for a
log-ratio design, and would welcome your comments on the feasibility and
benefits of the log-ratios approach.

Also, I was wanting to look at the profile of all probes across all reps
of all vegetations - the functions that are not changed between
ecosystems can be as informative as those that differ.  Is there a way
to remove the spot*dye effect from the normalised data from individual
arrays so I can make an equal comparison of values from all samples
using, for a heatmap for example, without dye effects confounding the
result. 

Thanks for your assistance.

Regards

Ross


On Fri, 2011-06-24 at 08:35 +1000, Gordon K Smyth wrote:

> 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@ecogeonomix.com>
> > To: bioconductor@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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