[NMF-user] setting the fraction of genes randomly sampled in an iteration?

[Renaud Gaujoux]

Hi,

no, all genes/features are included in each run. What changes is the seed,
i.e. starting point, which is different and randomly generated at each run.

Standard consensus clustering analysis would use a different set of
_samples_ for each run. This is fine for evaluating the accuracy/stability
of classification, but makes it difficult to link features to sample
groups, since each run (vote) returns a somehow different set of
component-specific feature: what set of features or basis components should
be used? average? consensus?
Would be nice to incorporate a function/option to  easily perform such
analysis though.

There is still some methodology to be developed around this point. A
technical issue also arise in term of memory/speed, if one wants to compute
complete feature consensus matrices.
I am happy to hear/discuss on this.
My time is currently very limited, although bringing the package back to
CRAN is quite high on my todo list.

Renaud



2013/3/14 Gordon Robertson <grobertson at bcgsc.ca>

> From what I understand, in each iteration (of, say, 200) in a run, a
> random subset of genes is used. Is it possible to set the fractional value
> retained, e.g. 0.90, 0.95?
> Thanks,
> G
> --
> Gordon Robertson
> Michael Smith Genome Sciences Centre
> BC Cancer Agency
> Vancouver BC Canada
> www.bcgsc.ca
>
>
>
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-- 

Renaud Gaujoux
Computational Biology - University of Cape Town
South Africa
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