[BioC] get over it/WAKE uP and SMELL the COFFEE

[A.J. Rossini]

Naomi Altman <naomi at stat.psu.edu> writes:

> Currently I am telling the biologists to consider microarrays as
> screening experiments.  Mostly, they use the results for second stage
> analyses, which may be:
>
> e.g. statistical analyses such as clustering etc
>        bioinformatics analyses such as GO, BLAST or sequence analyses
>        lab analyses such as Northern blots, in situs, etc
>
> Given the huge number of genes on most arrays, I do want a reasonably
> reliable method of screening.  On the other hand, I sometimes just
> rank the genes by test score, rather than attempt to determine some
> suitable alpha-level, FDR or FNR.
>
> Incidentally, distinguishing between technical replicates and
> biological replicates can make a huge different to ANOVA test scores,
> so I think we should insist that our analyses should account for this.

Slightly off topic, but one thing that I'm beginning to suspect is
that regardless of the statistical evidence, there are probably
publishable and plausible stories for approx 20-40% of the genes in
any experiment; the goal is to insure more (weak but useful) screening
evidence for finding those genes, not that the experiment can strongly
support the results, regardless.

It's the story-telling and plausibility (which are related to the
rationale behind the experiment) which drive the results, and that
isn't (and never really was) primarily a statistical issue, but
related to the scientific issues surrounding the experiment we are
working with, hence the reason to incorporate and use metadata as
primary (and I'm happy that Robert and others have been pushing this
issue a good deal in the design of Bioconductor tools).

best,
-tony

-- 
rossini at u.washington.edu            http://www.analytics.washington.edu/ 
Biomedical and Health Informatics   University of Washington
Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research Center
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