[BioC] dataset dim for siggenes

[ferreirafm at usp.br]

Hi Jim, 
Thanks for your message. For "validation" I meant to select 10 out of those 30 mirs to run another qPCR experiment for different samples keeping the same number of groups (4) and biological reps (15). My issue is how to select them. I was wondering which packages and tests else I could try in order to take the best 10 mirs for validation. So, I thought that would be useful to take the same mirs from different tests like ANOVA, SAM, LIMMA and others. Also, I would like to make sure I will run those tests using appropriated packages for that size data. From your answer, I understood it doesn't make sense. 
Best, 
Fred 

----- Mensagem original -----

> De: "James W. MacDonald" <jmacdon at uw.edu>
> Para: ferreirafm at usp.br
> Cc: "bioconductor" <bioconductor at r-project.org>
> Enviadas: Sexta-feira, 12 de Setembro de 2014 12:47:55
> Assunto: Re: [BioC] dataset dim for siggenes

> Hi Fred,

> I am assuming you have 116 miRNAs, and 60 samples. In which case you
> could probably just use a conventional t-test or linear model,
> although using limma wouldn't be a controversial decision. Not too
> sure about siggenes though. You have to estimate the proportion of
> true nulls, and I don't know if 116 comparisons are enough.

> But the larger question is the issue of running further statistical
> tests for validation. I am not sure what you mean by that.
> Quantitative PCR is (for better or worse) assumed to be the 'gold
> standard' for quantification of nucleic acid sequences, so there
> doesn't seem to be much more to do. Certainly re-running the
> analyses using a slightly different method isn't useful. That's like
> weighing yourself on a bunch of different scales; it tells you way
> more about the scales than it does about your weight.

> I think the next step (or really, the first step if you haven't
> already done so) is to ensure that your data meet all the underlying
> assumptions for linear modelling, so that you can have confidence in
> the conclusions you draw from the results.

> Best,

> Jim

> On Fri, Sep 12, 2014 at 11:18 AM, < ferreirafm at usp.br > wrote:

> > Hi list,
> 
> > I have a qPCR 116 x60 data set processed with limma. Results showed
> > 30 DE miRNAs. My idea is to pick-up 10 of them for validation
> > running further statistical tests and taking the most recurrent
> > mirs
> > from all analyses (does it make sense?). Well, I was thinking of
> > using siggenes, however, their authors recommend it for high-
> > dimensional data. Will siggenes be suitable for my data? if not,
> > could someone suggest others packages and perhaps tests more
> > appropriated to this size data?
> 
> > Best.
> 
> > Fred
> 
> > [[alternative HTML version deleted]]
> 

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> --

> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099

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