I can't see how the output of pamr.listgenes would be sufficient to reproduce a trained classifier. I think your only choice would be to re-run PAM starting from the CEL files. Also, consider whether their classifier would even be applicable to your microarray samples, since your samples and theirs are normalized separately. If you have a bunch of your own samples that you wish to classify, the correct approach might be to normalize the training samples and your samples together as one dataset and then re-train the classifier, rather than use the exact centroids computed on the original normalized data. In other words, repeating the training yourself may be the only statistically valid choice anyway. On Fri 17 May 2013 01:28:46 PM PDT, Ed Siefker wrote: > Can someone nudge me in the right direction here? Am I trying to do > something > that isn't possible? Am I trying to do something that's so obvious it > hasn't been > documented? Am I just unaware of where the appropriate documentation is? > Any advice would be greatly appreciated. Thanks > -Ed > > > On Wed, May 15, 2013 at 1:24 PM, Ed Siefker <ebs15242 at="" gmail.com=""> wrote: > >> I'm reading through some papers that use PAM to create a classifier from >> microarray data. >> I would like to use these classifiers to classify my own samples with >> microarray data. >> These papers publish the output of 'pamr.listgenes()', and it's not clear >> how to massage >> that into a format that 'pamr.predict()' will accept. >> >> The first argument to 'pamr.predict()' is "the result of a call to >> pamr.train". 'pamr.train()' >> operates on normalized microarray data and a vector of class labels. >> Essentially, I'd >> have to repeat the entire analysis, downloading every CEL file and >> normalizing it, >> in order to run 'pamr.train()' so I can run 'pamr.predict'. >> >> That doesn't seem like the right way to do things, but I can't find any >> other function >> that would create the "pamrtrained" object that 'pamr.predict()' >> requires. What's the >> right way to do what I want to do here? >> > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Hi Ed, There are multiple reasons why you are getting no traction. 1.) The pamr package isn't a Bioconductor package. Just because you are using microarrays doesn't mean this is a BioC question. 2.) You are basically telling us that you read something and the authors of what you read created something and you want it to be acceptable as input for a given function. Setting aside the non-BioC nature of your question, how is anybody on this list supposed to help? I guess we could track down the papers, or perhaps load up the pamr package and try to replicate what you are trying to do, but to quote Sweet Brown, "ain't nobody got time fo dat" (. This is why you are requested to include a small, complete code example of what you are trying to do, which would help people see what the problem is. You will probably get more traction on R-help, or by contacting the authors of the paper you are reading, or perhaps the authors of pamr (although good luck with that...). Best, Jim On 5/17/2013 4:28 PM, Ed Siefker wrote: > Can someone nudge me in the right direction here? Am I trying to do > something > that isn't possible? Am I trying to do something that's so obvious it > hasn't been > documented? Am I just unaware of where the appropriate documentation is? > Any advice would be greatly appreciated. Thanks > -Ed > > > On Wed, May 15, 2013 at 1:24 PM, Ed Siefker<ebs15242 at="" gmail.com=""> wrote: > >> I'm reading through some papers that use PAM to create a classifier from >> microarray data. >> I would like to use these classifiers to classify my own samples with >> microarray data. >> These papers publish the output of 'pamr.listgenes()', and it's not clear >> how to massage >> that into a format that 'pamr.predict()' will accept. >> >> The first argument to 'pamr.predict()' is "the result of a call to >> pamr.train". 'pamr.train()' >> operates on normalized microarray data and a vector of class labels. >> Essentially, I'd >> have to repeat the entire analysis, downloading every CEL file and >> normalizing it, >> in order to run 'pamr.train()' so I can run 'pamr.predict'. >> >> That doesn't seem like the right way to do things, but I can't find any >> other function >> that would create the "pamrtrained" object that 'pamr.predict()' >> requires. What's the >> right way to do what I want to do here? >> > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099