Thanks, Martin.  Based on my previous post, I thought of a more general
formulation of my question  that I think would be helpful to ask here.
What's the best way to build an R object that links multiple datapoints
about different people?  I mean, I happen to have datasets that have
individual gene expression data tied to individual patient characteristics
(how long they survived, age, gender, and other things).  But if I were a
baseball statistician wanting to link individual batting data to individual
baseball player characteristics (age, height, weight, etc), the idea would
be the same, I think.

I could do this in Python but I'm not really sure how it would work in R and
would greatly appreciate any advice about how to implement this.

Thanks!

Ben


On Fri, Oct 7, 2011 at 10:50 AM, Martin Morgan <mtmorgan@fhcrc.org> wrote:

> Hey Ben -- this is a bioconductor question so please ask on the
> Bioconductor mailing list. Good news, you don't have to subscribe to post
>
> http://bioconductor.org/help/**mailing-list/<http://bioconductor.org/help/mailing-list/>
>
> Martin
>
>
> On 10/07/2011 07:27 AM, Ben Ganzfried wrote:
>
>> Hey,
>>
>> I have code that can check the quality of a data set we're working with
>> (expression data), and I'm having some trouble writing code that would
>> make
>> the expression data we have tie to other data we want to link it to
>> (called
>> phenotype data).  Does anyone have any advice on how I could make a single
>> object that would do this?  Other relevant info: I want to use the pdata()
>> function, and I have all the phenotype data available as csv files.  Here
>> is
>> what I have in regards to the expression data:
>>
>> library(affy)
>> library(arrayQualityMetrics)
>>
>> inputargs<- commandArgs(TRUE)
>> print(inputargs)
>> sessionInfo()
>>
>> strInputAccession<- inputargs[1]
>> strBaseDir<- inputargs[2]
>>
>> processedDir<-
>> paste(strBaseDir,**strInputAccession,"PROCESSED/**DEFAULT",sep="/")
>> qcDir<- paste(strBaseDir,**strInputAccession,"QC",sep="/"**)
>> sampleFile<-
>> paste(processedDir,"/",**strInputAccession,"_**RAWfilenames.txt",sep="")
>> defaultExprs<-
>> paste(processedDir,"/",**strInputAccession,"_default_**exprs.csv",sep="")
>>
>> if(file.exists(sampleFile)){
>>   affyobj<- try(ReadAffy(filenames=read.**table(sampleFile,as.is
>> =T,header=FALSE)[,1]))
>>   if(class(affyobj)=="try-error"**){
>>     rm(affyobj)
>>   }else{
>>     sampleNames(affyobj)<- sub("\\..*","",sampleNames(**affyobj))  #get
>> rid
>> of .CEL etc extension, just keep GSM
>>   }
>> }
>>
>> if(!exists("affyobj")){
>>   mdExprs<- as.matrix(read.csv(**defaultExprs,row.names=1))
>>   if( min( mdExprs, na.rm = TRUE )>= 0&  max( mdExprs, na.rm = TRUE )>= 50
>>
>> ) {
>>     mdExprs<- log(mdExprs, base = 2) }
>>   affyobj<- new("ExpressionSet",exprs=**mdExprs)
>> }
>>
>> x<- arrayQualityMetrics(**expressionset = affyobj,
>>                          outdir=qcDir,
>>                          force = TRUE,
>>                          spatial=FALSE)
>>
>>        [[alternative HTML version deleted]]
>>
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>>
>
>
> --
> Computational Biology
> Fred Hutchinson Cancer Research Center
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>
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