[BioC] PAM: Applying published classifiers

[James W. MacDonald]

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" 
(http://www.youtube.com/watch?v=8cT_Ulmcrys). 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]]
>
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
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