<div dir="ltr"><div><div><div><div><div>Could you please post some reproducible example (commands, output of errors, traceback and sessionInfo)?<br></div>This will simplify tracing the issue.<br></div><div>If confidentiality is an issue, email only to me.<br>
<br></div>e.g.:<br><br></div># run NMF<br></div>res <- nmf(x, 2:6, .opt='v9')<br></div>traceback()<br>sessionInfo()<br><div><br></div><div>Thank you.<br></div><div>Renaud<br></div></div><div class="gmail_extra">
<br><br><div class="gmail_quote">2013/3/18 Gordon Robertson <span dir="ltr"><<a href="mailto:grobertson@bcgsc.ca" target="_blank">grobertson@bcgsc.ca</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="word-wrap:break-word">Renaud,<div><br></div><div>Thanks for clarifying this.</div><div><br></div><div>I asked because I tried to run NMF on a miRNA-seq abundance matrix that had 66 samples (columns) and only a small set of miRs (rows), say 20 miRs. I've used NMF routinely for larger miRNA-seq data matrices for some time (using 200-300 miRs), including on a 300-miR matrix for the same samples, but this time the survey returned only errors. I was able to get results from Matt Wilkerson's Consensus Cluster Plus package. I'll look more carefully at what happens to the NMF runs as I progressively remove miRs. </div>
<div><span class="HOEnZb"><font color="#888888"><br></font></span><div><span class="HOEnZb"><font color="#888888">G</font></span><div><div class="h5"><br><div>
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<br><div><div>On 2013-03-18, at 12:55 AM, Renaud Gaujoux wrote:</div><br><blockquote type="cite"><div dir="ltr"><div><div><div>Hi,<br><br></div>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.<br>
<br></div>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?<br>
</div><div>Would be nice to incorporate a function/option to easily perform such analysis though.<br></div><div><br></div>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.<br>
<div>I am happy to hear/discuss on this.<br></div><div>My time is currently very limited, although bringing the package back to CRAN is quite high on my todo list.<br><br></div><div>Renaud<br></div><div><br></div></div><div class="gmail_extra">
<br><br><div class="gmail_quote">2013/3/14 Gordon Robertson <span dir="ltr"><<a href="mailto:grobertson@bcgsc.ca" target="_blank">grobertson@bcgsc.ca</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="word-wrap:break-word"><div>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? </div><div>
Thanks,</div><div>G</div><div>
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--</div><div style="word-wrap:break-word">Gordon Robertson<br>Michael Smith Genome Sciences Centre</div><div style="word-wrap:break-word">BC Cancer Agency<br>Vancouver BC Canada<br><a href="http://www.bcgsc.ca/" target="_blank">www.bcgsc.ca</a><br>
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<pre cols="72">Renaud Gaujoux
Computational Biology - University of Cape Town
South Africa</pre>
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</blockquote></div><br></div></div></div></div></div></blockquote></div><br><br clear="all"><br>-- <br><pre cols="72">Renaud Gaujoux
Computational Biology - University of Cape Town
South Africa</pre>
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