[BioC] Separate channel analysis of two color micro arrays:avereps and intraspotCorrelation functions

[Gordon K Smyth]

Dear Rob,

Yes, estimate the intra spot correlation using the same data object as for 
the linear model.

My preference is usually not to average over multiple probes, but that is 
another matter.

Best wishes
Gordon

PS. When you give sessionInfo(), it should be for the session in which you 
ran the code given.  (In general, not important in this case.)


> Date: Wed, 13 Nov 2013 07:51:40 -0800 (PST)
> From: "Rob [guest]" <guest at bioconductor.org>
> To: bioconductor at r-project.org, robrobrab at hotmail.be
> Subject: [BioC] Separate channel analysis of two color micro arrays:
> 	avereps	and intraspotCorrelation functions
>
>
> Dear limma users and experts,
>
> I'm working with two color Agilent GE microarrays with a common reference set-up.
> Here, I'm using the avereps function to average multiple probes over a specific gene before fitting a linear model to the data.
>
> However, I'm getting low FDR corrected p-values over my whole data resulting in almost no differential expression.
> Trying to circumvent this, I've read that by applying the intraspotCorrelation function and performing a separate channel analysis,
> you can get better corrected p-values if the corr < 0.5 (as you are now also incorporating the information available in the A-values).
> I've done this and this is resolving the problem.
>
> My question specifically is now if I should do the intraspotCorrelation on the MA object after averaging the probes over a specific gene (on which the linear model will be based), or on the MA object before averaging?
>
> I think I should do the intraspotCorrelation function on the same MA object as the linear model will be fitted, but not sure.
>
>
> The two scenarios:
>
>
> 1. Using MA object after averaging over gene ID
>
> MA_norm<-normalizeBetweenArrays(MA_norm, method=between)
> MA_ave<-avereps(MA_norm, ID=MA_norm$genes$ID)
>
> corfit_Ave<-intraspotCorrelation(MA_ave, design)
> fit_ave<-lmscFit(MA_ave, design, correlation=corfit_Ave$consensus)
> eb_Ave<-eBayes(fit_Ave)
>
>
> 2. Using MA object before averaging over gene ID and using two different MA objects for the correlation estimate and linear model
>
> MA_norm<-normalizeBetweenArrays(MA_norm, method=between)
> MA_ave<-avereps(MA_norm, ID=MA_norm$genes$ID)
>
> corfit<-intraspotCorrelation(MA_norm, design)
> fit<-lmscFit(MA_ave, design, correlation=corfit$consensus)
> eb<-eBayes(fit)
>
>
>
> Any advice on this would be very welcome,
>
> thanks in advance,
> Rob.
>
>
> -- output of sessionInfo():
>
> R version 3.0.1 (2013-05-16)
> Platform: x86_64-w64-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=Dutch_Belgium.1252  LC_CTYPE=Dutch_Belgium.1252
> [3] LC_MONETARY=Dutch_Belgium.1252 LC_NUMERIC=C
> [5] LC_TIME=Dutch_Belgium.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> loaded via a namespace (and not attached):
> [1] tools_3.0.1
>
> --
> Sent via the guest posting facility at bioconductor.org.

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