loading processed data into eset class
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Zhe Liu ▴ 90
@zhe-liu-4199
Last seen 6.6 years ago
Dear all, I have a problem with loading matrix data into eset class object. The data.frame is used to load tab-delimited processed data. But when I want to run rma() method, there is always a error. Microarray data is downloaded from ArrayExpress, since my PC is not powerful to deal with raw data. I want to load processed data and then run it. Below is the code: library("affy") library("limma") arraydata=read.table(".\\E-MEXP-886.processed.1\\E-MEXP-886-processed- data-1 343526943.txt",sep="\t",quote="",row.names=1,header=TRUE) phenoData=read.table("E-MEXP-886.sdrf.txt",sep="\t",quote="",header=TR UE,row .names=1) featureData=read.AnnotatedDataFrame("A-AFFY-23.adf.txt",sep="\t",quote ="",he ader=TRUE,fill=TRUE,skip=13) #The blue part fetch Affymetrix:CHPSignal column of the processed data, since the data format is as below: #Scan REF H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 #Composite Element REF Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-5_at AFFX-BioB-5_at 20 20 39.7 Present 0.00256 AFFX-BioB-5_at 20 20 60.1 Present 0.00125 AFFX-BioB-5_at 20 20 42.2 Present 0.00256 AFFX-BioB-5_at 20 20 93.5 Present 0.0003 AFFX-BioB-5_at 20 20 44.9 Present 0.00227 AFFX- BioB-5_at 20 20 75.3 Present 0.00097 AFFX-BioB-5_at 20 20 42.9 Present 0.00097 AFFX-BioB-5_at 20 20 53.7 Present 0.00159 AFFX-BioB-5_at 20 20 34.7 Present 0.00125 AFFX-BioB-5_at 20 20 93.8 Present 0.00034 #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-M_at AFFX-BioB-M_at 20 20 61.1 Present 0.00017 AFFX-BioB-M_at 20 20 93.1 Present 0.00045 AFFX-BioB-M_at 20 20 70.2 Present 0.0002 AFFX-BioB-M_at 20 20 173.3 Present 5e-05 AFFX-BioB-M_at 20 20 59.7 Present 0.00013 AFFX- BioB-M_at 20 20 113.9 Present 0.00011 AFFX-BioB-M_at 20 20 75.5 Present 0.00039 AFFX-BioB-M_at 20 20 77.5 Present 0.00017 AFFX-BioB-M_at 20 20 68.7 Present 0.00026 AFFX-BioB-M_at 20 20 157.9 Present 7e-05 #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-3_at AFFX-BioB-3_at 20 20 39 Present 0.00141 AFFX-BioB-3_at 20 20 49.6 Present 0.0011 AFFX-BioB-3_at 20 20 35.8 Present 0.00934 AFFX-BioB-3_at 20 20 69.6 Present 5e-05 AFFX-BioB-3_at 20 20 36 Present 0.00141 AFFX- BioB-3_at 20 20 73.2 Present 0.00066 AFFX-BioB-3_at 20 20 38.8 Present 0.0018 AFFX-BioB-3_at 20 20 39.1 Present 0.00097 AFFX-BioB-3_at 20 20 39.9 Present 0.00086 AFFX-BioB-3_at 20 20 75.9 Present 4e-05 sub_title=seq(1:10)*6-2 arraydata_pro=arraydata[,sub_title] #replace the unmatched parts of names sub_title_name=gsub("^H\\_|\\.[0-9]$","",colnames(arraydata[,sub_title ])) sub_title_name=gsub("\\.","-",sub_title_name) colnames(arraydata_pro)=sub_title_name #sort arraydata in the same order with phenoData arraydata_pro=arraydata_pro[,order(colnames(arraydata_pro))] #convert data.frame into AnnotatedDataFrame phenoData=phenoData[order(row.names(phenoData)),] phenoData=new("AnnotatedDataFrame", data=phenoData) experimentData=read.MIAME("E-MEXP-886.idf.txt") Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, experimentData=experimentData, exprs=as.matrix(arraydata_pro)) rEset=rma(Eset) when I run the final line, it reported: Error in function (classes, fdef, mtable) : unable to find an inherited method for function "probeNames", for signature "ExpressionSet" I am really confusing about this error. Do you have the same experiences? Thanks a lot! Zhe [[alternative HTML version deleted]]
ArrayExpress ArrayExpress • 2.3k views
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@vincent-j-carey-jr-4
Last seen 20 hours ago
United States
This error is easy to reproduce > data(sample.ExpressionSet) > rma(sample.ExpressionSet) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "probeNames", for signature "ExpressionSet" it is not a great error message but perhaps no one would anticipate that you would try to preprocess an ExpressionSet. rma() accepts AffyBatch instances in the typical workflow > sessionInfo() R version 2.11.1 Patched (2010-06-08 r52234) x86_64-apple-darwin10.3.0 locale: [1] C attached base packages: [1] stats graphics grDevices datasets tools utils methods [8] base other attached packages: [1] affy_1.26.0 Biobase_2.8.0 weaver_1.14.0 codetools_0.2-2 [5] digest_0.4.2 loaded via a namespace (and not attached): [1] affyio_1.16.0 preprocessCore_1.10.0 On Thu, Aug 5, 2010 at 7:11 AM, Zhe Liu <zhliu.tju@gmail.com> wrote: > Dear all, > > > > I have a problem with loading matrix data into eset class object. The > data.frame is used to load tab-delimited processed data. But when I want to > run rma() method, there is always a error. > > > > Microarray data is downloaded from ArrayExpress, since my PC is not > powerful > to deal with raw data. I want to load processed data and then run it. > > > > Below is the code: > > > > library("affy") > > library("limma") > > > arraydata=read.table(".\\E-MEXP-886.processed.1\\E-MEXP-886 -processed-data-1 > 343526943.txt",sep="\t",quote="",row.names=1,header=TRUE) > > > phenoData=read.table("E-MEXP-886.sdrf.txt",sep="\t",quote="",header= TRUE,row > .names=1) > > > featureData=read.AnnotatedDataFrame("A-AFFY-23.adf.txt",sep="\t",quo te="",he > ader=TRUE,fill=TRUE,skip=13) > > > > #The blue part fetch Affymetrix:CHPSignal column of the processed data, > since the data format is as below: > > #Scan REF H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 > H_ataxin1WT-1753 H_ataxin1WT-1753 H_ataxin1WT-1753 H_Ataxin1WT-1756 > H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 H_Ataxin1WT-1756 > H_Ataxin1WT-1756 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 > H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1863 H_Ataxin1WT-1869 > H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 H_Ataxin1WT-1869 > H_Ataxin1WT-1869 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 > H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1WT-1364 H_Ataxin1KO-1307 > H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 H_Ataxin1KO-1307 > H_Ataxin1KO-1307 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 > H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1749 H_Ataxin1KO-1750 > H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 H_Ataxin1KO-1750 > H_Ataxin1KO-1750 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 > H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1751 H_Ataxin1KO-1919 > H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 H_Ataxin1KO-1919 > H_Ataxin1KO-1919 > > #Composite Element REF Affymetrix:CHPProbeSetName Affymetrix:CHPPairs > Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection > Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName > Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal > Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue > Affymetrix:CHPProbeSetName Affymetrix:CHPPairs > Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection > Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName > Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal > Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue > Affymetrix:CHPProbeSetName Affymetrix:CHPPairs > Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection > Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName > Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal > Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue > Affymetrix:CHPProbeSetName Affymetrix:CHPPairs > Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection > Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName > Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal > Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue > Affymetrix:CHPProbeSetName Affymetrix:CHPPairs > Affymetrix:CHPPairsUsed Affymetrix:CHPSignal Affymetrix:CHPDetection > Affymetrix:CHPDetectionPvalue Affymetrix:CHPProbeSetName > Affymetrix:CHPPairs Affymetrix:CHPPairsUsed Affymetrix:CHPSignal > Affymetrix:CHPDetection Affymetrix:CHPDetectionPvalue > > #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-5_at AFFX-BioB-5_at 20 > 20 39.7 Present 0.00256 AFFX-BioB-5_at 20 20 60.1 > Present 0.00125 AFFX-BioB-5_at 20 20 42.2 Present > 0.00256 AFFX-BioB-5_at 20 20 93.5 Present 0.0003 > AFFX-BioB-5_at 20 20 44.9 Present 0.00227 AFFX- BioB-5_at > 20 20 75.3 Present 0.00097 AFFX-BioB-5_at 20 20 > 42.9 Present 0.00097 AFFX-BioB-5_at 20 20 53.7 Present > 0.00159 AFFX-BioB-5_at 20 20 34.7 Present 0.00125 > AFFX-BioB-5_at 20 20 93.8 Present 0.00034 > > #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-M_at AFFX-BioB-M_at 20 > 20 61.1 Present 0.00017 AFFX-BioB-M_at 20 20 93.1 > Present 0.00045 AFFX-BioB-M_at 20 20 70.2 Present > 0.0002 AFFX-BioB-M_at 20 20 173.3 Present 5e-05 > AFFX-BioB-M_at 20 20 59.7 Present 0.00013 AFFX- BioB-M_at > 20 20 113.9 Present 0.00011 AFFX-BioB-M_at 20 20 > 75.5 Present 0.00039 AFFX-BioB-M_at 20 20 77.5 Present > 0.00017 AFFX-BioB-M_at 20 20 68.7 Present 0.00026 > AFFX-BioB-M_at 20 20 157.9 Present 7e-05 > > #Affymetrix:CompositeSequence:MOE430A:AFFX-BioB-3_at AFFX-BioB-3_at 20 > 20 39 Present 0.00141 AFFX-BioB-3_at 20 20 49.6 > Present 0.0011 AFFX-BioB-3_at 20 20 35.8 Present > 0.00934 AFFX-BioB-3_at 20 20 69.6 Present 5e-05 > AFFX-BioB-3_at 20 20 36 Present 0.00141 AFFX- BioB-3_at > 20 20 73.2 Present 0.00066 AFFX-BioB-3_at 20 20 > 38.8 Present 0.0018 AFFX-BioB-3_at 20 20 39.1 Present > 0.00097 AFFX-BioB-3_at 20 20 39.9 Present 0.00086 > AFFX-BioB-3_at 20 20 75.9 Present 4e-05 > > > > sub_title=seq(1:10)*6-2 > > arraydata_pro=arraydata[,sub_title] > > #replace the unmatched parts of names > > sub_title_name=gsub("^H\\_|\\.[0-9]$","",colnames(arraydata[,sub_tit le])) > > sub_title_name=gsub("\\.","-",sub_title_name) > > colnames(arraydata_pro)=sub_title_name > > #sort arraydata in the same order with phenoData > > arraydata_pro=arraydata_pro[,order(colnames(arraydata_pro))] > > #convert data.frame into AnnotatedDataFrame > > phenoData=phenoData[order(row.names(phenoData)),] > > phenoData=new("AnnotatedDataFrame", data=phenoData) > > experimentData=read.MIAME("E-MEXP-886.idf.txt") > > Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, > experimentData=experimentData, exprs=as.matrix(arraydata_pro)) > > rEset=rma(Eset) > > > > when I run the final line, it reported: > > Error in function (classes, fdef, mtable) : > > unable to find an inherited method for function "probeNames", for signature > "ExpressionSet" > > > > I am really confusing about this error. Do you have the same experiences? > > > > Thanks a lot! > > > > Zhe > > > > > > > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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@alex-gutteridge-2935
Last seen 6.6 years ago
United States
On Thu, 5 Aug 2010 15:11:34 +0100, "Zhe Liu" <zhliu.tju at="" gmail.com=""> wrote: > I have a problem with loading matrix data into eset class object. The > data.frame is used to load tab-delimited processed data. But when I want to > run rma() method, there is always a error. > > Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, > experimentData=experimentData, exprs=as.matrix(arraydata_pro)) > > rEset=rma(Eset) > > when I run the final line, it reported: > > Error in function (classes, fdef, mtable) : > > unable to find an inherited method for function "probeNames", for signature > "ExpressionSet" rma() works on AffyBatch objects and creates ExpressionSet objects - hence the error. If the data is already processed why do you want to run rma? -- Alex Gutteridge
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Thanks a lot for your soon reply. I think this processed data in arrayexpress is not background corrected and normalized, though it should be as normalized in MIAME data format. Because the CHP:singal amount vary greatly. For example as below, some of them are less than 100 but some of them are over thousands. 51.2 78 64.6 136.3 191.2 530.7 589.4 2594.6 2853.1 I think they should be normalized. Also I checked the idf file and there is not indication about the method about normalization. So is there any method applicable to eset? Thanks. Zhe -----Original Message----- From: Alex Gutteridge [mailto:alexg@ruggedtextile.com] Sent: 05 August 2010 15:59 To: Zhe Liu Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] loading processed data into eset class On Thu, 5 Aug 2010 15:11:34 +0100, "Zhe Liu" <zhliu.tju at="" gmail.com=""> wrote: > I have a problem with loading matrix data into eset class object. The > data.frame is used to load tab-delimited processed data. But when I want to > run rma() method, there is always a error. > > Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, > experimentData=experimentData, exprs=as.matrix(arraydata_pro)) > > rEset=rma(Eset) > > when I run the final line, it reported: > > Error in function (classes, fdef, mtable) : > > unable to find an inherited method for function "probeNames", for signature > "ExpressionSet" rma() works on AffyBatch objects and creates ExpressionSet objects - hence the error. If the data is already processed why do you want to run rma? -- Alex Gutteridge
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You are putting your data into an ExpressionSet. However, rma takes and AffyBatch as input and outputs an ExpressionSet, so you are using the wrong class. Having said that, you can usually find CEL files on ArrayExpress, which will make your life much easier. Kasper On Thu, Aug 5, 2010 at 11:19 AM, Zhe Liu <zhliu.tju at="" gmail.com=""> wrote: > Thanks a lot for your soon reply. > > I think this processed data in arrayexpress is not background corrected and > normalized, though it should be as normalized in MIAME data format. Because > the CHP:singal amount vary greatly. For example as below, some of them are > less than 100 but some of them are over thousands. > > 51.2 > 78 > 64.6 > 136.3 > 191.2 > 530.7 > 589.4 > 2594.6 > 2853.1 > > I think they should be normalized. Also I checked the idf file and there is > not indication about the method about normalization. So is there any method > applicable to eset? > > Thanks. > > Zhe > > -----Original Message----- > From: Alex Gutteridge [mailto:alexg at ruggedtextile.com] > Sent: 05 August 2010 15:59 > To: Zhe Liu > Cc: bioconductor at stat.math.ethz.ch > Subject: Re: [BioC] loading processed data into eset class > > On Thu, 5 Aug 2010 15:11:34 +0100, "Zhe Liu" <zhliu.tju at="" gmail.com=""> wrote: >> I have a problem with loading matrix data into eset class object. The >> data.frame is used to load tab-delimited processed data. But when I want > to >> run rma() method, there is always a error. >> >> Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, >> experimentData=experimentData, exprs=as.matrix(arraydata_pro)) >> >> rEset=rma(Eset) >> >> when I run the final line, it reported: >> >> Error in function (classes, fdef, mtable) ?: >> >> unable to find an inherited method for function "probeNames", for > signature >> "ExpressionSet" > > rma() works on AffyBatch objects and creates ExpressionSet objects - hence > the error. > > If the data is already processed why do you want to run rma? > > -- > Alex Gutteridge > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
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Thanks a lot all of your helpful suggestions. Yeah, I have struggled with this things for couples of days, I should have an alternative way to make the life easier as u said. Zhe On 5 August 2010 18:59, Kasper Daniel Hansen <kasperdanielhansen@gmail.com>wrote: > You are putting your data into an ExpressionSet. However, rma takes > and AffyBatch as input and outputs an ExpressionSet, so you are using > the wrong class. > > Having said that, you can usually find CEL files on ArrayExpress, > which will make your life much easier. > > Kasper > > On Thu, Aug 5, 2010 at 11:19 AM, Zhe Liu <zhliu.tju@gmail.com> wrote: > > Thanks a lot for your soon reply. > > > > I think this processed data in arrayexpress is not background corrected > and > > normalized, though it should be as normalized in MIAME data format. > Because > > the CHP:singal amount vary greatly. For example as below, some of them > are > > less than 100 but some of them are over thousands. > > > > 51.2 > > 78 > > 64.6 > > 136.3 > > 191.2 > > 530.7 > > 589.4 > > 2594.6 > > 2853.1 > > > > I think they should be normalized. Also I checked the idf file and there > is > > not indication about the method about normalization. So is there any > method > > applicable to eset? > > > > Thanks. > > > > Zhe > > > > -----Original Message----- > > From: Alex Gutteridge [mailto:alexg@ruggedtextile.com] > > Sent: 05 August 2010 15:59 > > To: Zhe Liu > > Cc: bioconductor@stat.math.ethz.ch > > Subject: Re: [BioC] loading processed data into eset class > > > > On Thu, 5 Aug 2010 15:11:34 +0100, "Zhe Liu" <zhliu.tju@gmail.com> > wrote: > >> I have a problem with loading matrix data into eset class object. The > >> data.frame is used to load tab-delimited processed data. But when I want > > to > >> run rma() method, there is always a error. > >> > >> Eset=new("ExpressionSet", phenoData=phenoData, featureData=featureData, > >> experimentData=experimentData, exprs=as.matrix(arraydata_pro)) > >> > >> rEset=rma(Eset) > >> > >> when I run the final line, it reported: > >> > >> Error in function (classes, fdef, mtable) : > >> > >> unable to find an inherited method for function "probeNames", for > > signature > >> "ExpressionSet" > > > > rma() works on AffyBatch objects and creates ExpressionSet objects - > hence > > the error. > > > > If the data is already processed why do you want to run rma? > > > > -- > > Alex Gutteridge > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > [[alternative HTML version deleted]]
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