

Thank you James is is exactly what I needed.

Was comparing two groups and when I got back to the project some weeks later changed the package and got different results which left me a bit puzzled.

> Date: Tue, 12 Feb 2013 09:25:11 -0500
> From: jmacdon@uw.edu
> To: tintin_jb@hotmail.com
> CC: bioconductor@r-project.org
> Subject: Re: [BioC] Differences in results analyzing Mouse Gene 1.0-ST using oligo and affy package
> 
> Hi Jon,
> 
> On 2/11/2013 7:40 PM, Jon Toledo wrote:
> >
> >
> >
> > Dear Bioconductor List,
> >
> >
> > I have repeated my workflow using the affy and oligo package alternatively followed by the limma   package to analyze and experiment with two conditions  using Mouse Gene 1.0-ST chips and I arrive to different results.
> >
> > I have been looking online and found that at least for the  for the Mouse Gene 1.1-ST the oligo package is preferred, but not anything clear for Mouse Gene 1.0-ST .
> >
> > I attach below my code and session info:
> >
> > A) For running affy:
> >
> > library(affy)
> > library(pd.mogene.1.0.st.v1)
> > library(mogene10sttranscriptcluster.db)
> >
> > cellist=list.celfiles(full.names=T)
> > cellistD14=grep("CD1...14",cellist,value=T)
> > esetD14<- justRMA(filenames=gsub("\\./","",cellistD14))
> >
> > B) For runnign oligo:
> >
> > library(oligo)
> > library(pd.mogene.1.0.st.v1)
> > library(mogene10sttranscriptcluster.db)
> >
> > cellist=list.celfiles(full.names=T)
> > cellistD14=grep("CD1...14",cellist,value=T)
> >
> > rsetD14=read.celfiles(cellistD14)
> > esetpD14=rma(rsetD14,target="probeset")
> > esetD14=rma(rsetD14,target="core")
> >
> >
> > C) Finally running the same analysis:
> >
> > designD14<-read.delim('designD14.txt')
> > contrast.matrix=model.matrix(~treatment,data=designD14)
> > library(limma)
> > fit<- lmFit(esetD14, contrast.matrix)
> > fit<- eBayes(fit,proportion=0.01)
> >
> > m1=topTable(fit, coef="treatment",number=1e8,adjust.method="BH")
> > m1=m1[,c("ID","logFC","P.Value","adj.P.Val")]
> > m1=cbind(m1[,1:2],FCTreat=2**m1[,2],PTreat=m1[,3],adj.PTreat=m1[,4])
> 
> You show a lot of code, but paradoxically for all that code I am left 
> wondering what you did, and how things differed.
> 
> Anyway, either oligo or xps are the only packages that were designed for 
> these arrays. The affy package was 'fixed' so it will work, but IMO you 
> should be using oligo or xps, not affy. There are differences between 
> the results for oligo and affy:
> 
>  > library(oligo)
>  > oligo <- rma(read.celfiles(list.celfiles()))
>  > library(affy)
>  > affy <- justRMA()
>  > oligo
> ExpressionSet (storageMode: lockedEnvironment)
> assayData: 35556 features, 16 samples
>    element names: exprs
> protocolData
>    rowNames: RH_8500.1_pid1257.CEL RH_8500.4_pid1257.CEL ...
>      RH_8867.8_pid1257.CEL (16 total)
>    varLabels: exprs dates
>    varMetadata: labelDescription channel
> phenoData
>    rowNames: RH_8500.1_pid1257.CEL RH_8500.4_pid1257.CEL ...
>      RH_8867.8_pid1257.CEL (16 total)
>    varLabels: index
>    varMetadata: labelDescription channel
> featureData: none
> experimentData: use 'experimentData(object)'
> Annotation: pd.mogene.1.0.st.v1
>  > affy
> ExpressionSet (storageMode: lockedEnvironment)
> assayData: 34760 features, 16 samples
>    element names: exprs, se.exprs
> protocolData
>    sampleNames: RH_8500.1_pid1257.CEL RH_8500.4_pid1257.CEL ...
>      RH_8867.8_pid1257.CEL (16 total)
>    varLabels: ScanDate
>    varMetadata: labelDescription
> phenoData
>    sampleNames: RH_8500.1_pid1257.CEL RH_8500.4_pid1257.CEL ...
>      RH_8867.8_pid1257.CEL (16 total)
>    varLabels: sample
>    varMetadata: labelDescription
> featureData: none
> experimentData: use 'experimentData(object)'
> Annotation: mogene10stv1
> 
> Note that oligo returns 35556 probesets, where affy returns 34760. If we 
> subset to overlapping probesets, we still get some differences:
> 
>  > oligo <- exprs(oligo)[featureNames(oligo) %in% featureNames(affy),]
>  > affy <- exprs(affy)[featureNames(affy) %in% row.names(oligo),]
>  > all.equal(row.names(oligo), row.names(affy))
> [1] TRUE
>  > all.equal(affy, oligo)
> [1] "Mean relative difference: 0.01049026"
> 
> And if I look at a summary of these differences, most are very small 
> (the IQR goes from 0.005 - 0.04 or so), with large differences at the 
> extremes (-5, 10). This may be due to differences in the probesets; note 
> that the affy package uses an old unsupported CDF file, whereas the 
> oligo package uses the current pgf/clf files.
> 
> Best,
> 
> Jim
> 
> 
> 
> 
> >
> >> sessionInfo() (This is for the affy run)
> > R version 2.15.2 (2012-10-26)
> > Platform: x86_64-w64-mingw32/x64 (64-bit)
> >
> > locale:
> > [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
> > [4] LC_NUMERIC=C                           LC_TIME=English_United States.1252
> >
> > attached base packages:
> > [1] stats     graphics  grDevices utils     datasets  methods   base
> >
> > other attached packages:
> >   [1] mogene10stv1cdf_2.11.0               limma_3.14.4                         mogene10sttranscriptcluster.db_8.0.1
> >   [4] org.Mm.eg.db_2.8.0                   AnnotationDbi_1.20.3                 pd.mogene.1.0.st.v1_3.8.0
> >   [7] oligo_1.22.0                         oligoClasses_1.20.0                  RSQLite_0.11.2
> > [10] DBI_0.2-5                            affy_1.36.1                          Biobase_2.18.0
> > [13] BiocGenerics_0.4.0
> >
> > loaded via a namespace (and not attached):
> >   [1] affxparser_1.30.2     affyio_1.26.0         BiocInstaller_1.8.3   Biostrings_2.26.3     bit_1.1-9             codetools_0.2-8
> >   [7] ff_2.2-10             foreach_1.4.0         GenomicRanges_1.10.6  IRanges_1.16.4        iterators_1.0.6       parallel_2.15.2
> > [13] preprocessCore_1.20.0 splines_2.15.2        stats4_2.15.2         tools_2.15.2          zlibbioc_1.4.0
> >
> > Thank you very much
> >
> >
> > J Toledo
> > UPenn
> > USA
> >
> >   		 	   		
> > 	[[alternative HTML version deleted]]
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@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
> 
 		 	   		  
	[[alternative HTML version deleted]]

