[BioC] help with paired-test in limma

[Saurin D. Jani]

Hi Ariel,

LIMMA is great for testing 2 conditions such as TreatmentA Vs TreatmentB 

You can also do t-test online for free..using parameters ,FDR(BH-method),
FoldChange and pvalue for your data.

e.g. 
     Give me genes from my data (TreatmentA vs TreatmentB)  < 0.01 pvalue 
                              and/or
     Give me genes from my data (TreatmentA vs TreatmentB)  < 0.01 FDR         
                 
                              and/or
     Give me genes from my data (TreatmentA vs TreatmentB)  > 3    FoldChange


ArrayQuest is free to access and register. Its free and safe..below is the link:

http://proteogenomics.musc.edu/quickSite/arrayQuest.php?page=home&act=manage

Resulting Report: It will also gives you Ontology analysis,Annotation
report,Heatmap, Pathway Heatmaps,Boxplots+Histograms(before and after
normalization).

ArrayQuest is free to access and register. It will allow you to upload your data
online and won't publish to public at all. Sort of like yahoo mail , you keep
your data..! Check it out..!

Saurin



Quoting Ariel Chernomoretz <ariel.chernomoretz at crchul.ulaval.ca>:

> Dear list,
> 
> This is my first time with limma (and also with linear models!).
> After reading the vignettes, papers and previously posted messages I came out
> 
> with something, but I am not sure it is the correct procedure....in
> particular 
> I have doubts related to how to handle with limma the paired nature of my 
> design. I would greatly appreciate some remarks, comments, help, etc. 
> 
> I want to study the differences between two treatments: allocationA and 
> allocationB, using single color affymetrix arrays, in a paired design.
> Two samples were taken from 13 patients, one before, and the other after 
> treatment. There were 6 and 7 patients treated with allocationA, and 
> allocationB, respectively. Each sample was technically replicated, so at the
> 
> end we have 4 chip per patient.
> The experimental setup looks like this:
> 
> Sample	BeforeAfter	Patient	allocation
> 1		0			1		A
> 2		0			1		A
> 3		1			1		A
> 4		1			1		A
> 5		0			2		A
> 6		0			2		A
> 7		1			2		A
> 8		1			2		A
> 9		0			3		B
> 10		0			3		B
> 11		1			3		B
> 12		1			3		B
> .		.			.		.
> .		.			.		.
> 52		1			13		B
> 
> 
> After reading some posts I decided to take the average of technical 
> replicates. Then I used a block in order to get the lmFit, and I calculated 
> the contrasts of interest:
> > design<-model.matrix(~allocation*AntesDespues,data=pData(eset))
> > bblock<-rep(1:13,each=2)
> > fit2<-lmFit(eset,design,block=bblock)
> >
> > cont.matrix<-cbind(A.AfterVSBefore=c(0,0,1,0),
> >                                    B.AfterVSBefore=c(0,0,1,1),
> >                                    Interac                =c(0,0,0,1))
> > fit2<-contrasts.fit(fit2,cont.matrix)
> > fit2eb<-eBayes(fit2)
> 
> I think that in this way I am implicitly assuming an intrablock correlation 
> level of 0.75. Is this somehow appropriate in order to consider this 
> a paired t-test like analysis? 
> 
> With duplicateCorrelation, I get a value of 0.21 (!)
> Is this value the one I should consider for lmFit ?
> Using this rather low value isn't like disregarding
> the before-after pairing for each patient?  Shouldn't I tweak the correlation
> 
> level to an artificially higher value (for instance 0.9) instead?
> 
> Any comments would be highly appreciated
> Thanks
> Ariel./
> 
> 
> 
> 
> 
> 
> -- 
> Ariel Chernomoretz, Ph.D.
> Centre de recherche du CHUL
> 2705 Blv Laurier, bloc T-367
> Sainte-Foy, Qc
> G1V 4G2
> (418)-525-4444 ext 46339
> 
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