[BioC] dataset dim for siggenes

[James W. MacDonald]

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
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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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