On Thu, Dec 12, 2013 at 9:50 AM, January Weiner <january.weiner@gmail.com>wrote:

> Dear all,
>
> I'm trying to analyse a data set reposited in GEO. In theory, I could
> download the CEL files and do everything from scratch, but I would prefer
> to use the GEO-reposited, normalized data.
>
> I load the data with
>
> library( GEOquery )
> g <- getGEO( "GSE31348" )[[1]]
>
> So far, so good. The data is supposed to be GCrma-normalized. Density plots
> show that the arrays have a symmetrical distribution around 0, so they are
> not logarithmized signals.
>

Actually, according to the metadata associated with the samples, they are:

"GC-RMA and baseline normalised to median"


>
> My question is how should I proceed with the data if I want to use limma to
> analyse it? Or should I use the original CEL files and a standard approach
> (as described, for example, in the limma guide)?
>
> If I dumbly run limma, I am getting more or less the results I expect, but
> the p-values seem to me overly optimistic:
>
> g2 <- new( "EList", list( targets= as( phenoData( g ), "data.frame" ),
> genes= as( featureData( g ), "data.frame" ), E= exprs( g ) ) )
> g2$targets$tp <- factor( gsub( "time point: Week ", "tp.",
> g2$targets$characteristics_ch1.2 ) )
> d <- model.matrix( ~ 0 + g2$targets$tp )
> colnames( d ) <- levels( g2$targets$tp )
> fit <- eBayes( contrasts.fit( lmFit( g2, d ), makeContrasts( "tp.0-tp.26",
> levels= d ) )
>
> I think that this is incorrect.
>

The answer to your question may depend on how much you care about the
specifics of the results.  If you are going to make costly or important
decisions based on the your analysis, starting with raw data is usually
best.  Some good QC and a search for batch effects may also help.

Sean

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