Hi James,

yes, we do see a similar response in other data sets (with more samples)
that we and others have generated, but it still feels a bit weird.

I have omitted the question of pairing for the sake of the simplicity here,
but I agree that I need to include this in the calculations. I use
duplicateCorrelation for that.

@Sean:

no, it's not so important -- I just use it for the sake of completeness.

Kind regards,

j.


On 12 December 2013 16:20, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi January,
>
> I think what you are doing is probably fine. From the paper:
>
> "Affymetrix CEL files were imported, summarized using the GC-RMA
> algorithm, and baseline-normalized to the median of all arrays."
>
> So that's pretty close to what you would get if you just took the celfiles
> yourself and ran them through affy or gcrma (or xps or frma or SCAN.UPC),
> except for the location change from shifting the data by the median. And I
> wouldn't be too surprised by very small p-values because you have a) 27
> samples/group, and b) they have tuberculosis, which I would expect to get
> the various white blood cells really worked up.
>
> However, I believe you are ignoring the fact that these data are paired,
> and should be including a 'patient' factor to account for the pairing.
>
> Best,
>
> Jim
>
>
>
> On 12/12/2013 9:50 AM, January Weiner 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.
>>
>> 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.
>>
>> Kind regards,
>> j.
>>
>>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>


-- 
-------- January Weiner --------------------------------------

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