[BioC] GOStat and multiple testing

[A.J. Rossini]

Might want to look at the social network literature -- something like
a p-* model for adjustment of the variance, and possibly looking at
the adjoint graph (flipping nodes/edges) might produce reasonably
adjusted coefficients.

best,
-tony


Nicholas Lewin-Koh <nikko at hailmail.net> writes:

> Hi,
> I have been thinking about the problem a little differently, and I am
> working on a write-up. 
>
> The first approach is, rather than testing each node independently,
> fit the whole go tree as a logistic regression with the response a {0,1}
> for each gene (ie expressed, not} and the predictors the whole go-tree.
> Obviously that alone is not the solution, but if we treat this as a
> spatial problem (the dag being the spatial join matrix) we can create a
> set of difference penalties on the coefficients that are dictated by the
> dag structure. Then we can do inference on the the beta's to see which
> terms are influential. One would fit a separate model for each category,
> Cellular component, biological Process, Molecular function.
>
> The second approach, that I haven't developed as far, is to think of
> each gene as starting at the root, and look at its survival along 
> paths in the dag, so that the aggregate is some sort of branching
> process. I don't know yet if this model is useful.
>
> Also inherent in the analysis is a potentially huge bias due to
> unannotated genes. I was thinking of approaching this using a kind of
> mark-recapture approach on the terms, kind of like the stochastic
> abundance models they use in ecology to predict the number of species in
> a community. We can come up with a bias correction if we have a term
> abundance distribution for the GO-classes.
>
> The logistic model is something I am actively working on, the rest are
> half baked thoughts I have been diddling with and haven't had time to
> chase too far.
>
> Nicholas
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>

-- 
Anthony Rossini			    Research Associate Professor
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
UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable
FHCRC  (M/W): 206-667-7025 FAX=206-667-4812 | use Email

CONFIDENTIALITY NOTICE: This e-mail message and any attachme...{{dropped}}