Dear list: I am performing microarray data analysis for the first time, so please forgive me if my question is inapproppriate. I am analyzing Affymetrix GeneChip Human Gene 1.0 ST Array and I found that "oligo package" is ideal for this type of arrays. Then, I used it and I started with the guide of V5ExonGene.pdf that I downloaded from Bioconductor. I read a post from the Bioconductor Mailing List Archives that said that "probeset" and "core" map to exons and genes respectively. Thus, why core expression and probeset expression showed the same values? I checked mappedkeys and they are different for both of them. Moreover I would like to study at the exon level my data, for that kind of analysis is it right to use the probeset expression? Thanks in advance. Ire P.S. Here is my code: #R version 2.12.0 (2010-10-15) #Platform: x86_64-unknown-linux-gnu (64-bit) ### Libraries library("affy") library("limma") #library("hugene10stv1cdf") library("oligo") library("hugene10stprobeset.db") library("hugene10sttranscriptcluster.db") library("pd.hugene.1.0.st.v1") library("pd.hugene.1.1.st.v1") library("IRanges") library("affxparser") library("Biobase") library("preprocessCore") library("Biostrings") #================================================== ### Open files and read files archCEL<-list.celfiles("~/Documents/MICROARRAYS/MicroarraysHebeData_An alisis/rawData", full.names=TRUE) affyHebe <- read.celfiles(archCEL) #================================================== ### rma genePS<-rma(affyHebe, target="probeset") geneCore<-rma(affyHebe, target="core") genePS #ExpressionSet (storageMode: lockedEnvironment) #assayData: 257430 features, 20 samples #  element names: exprs #protocolData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: exprs dates #  varMetadata: labelDescription channel #phenoData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: index #  varMetadata: labelDescription channel #featureData: none #experimentData: use 'experimentData(object)' #Annotation: pd.hugene.1.0.st.v1 geneCore #ExpressionSet (storageMode: lockedEnvironment) #assayData: 33297 features, 20 samples #  element names: exprs #protocolData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: exprs dates #  varMetadata: labelDescription channel #phenoData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: index #  varMetadata: labelDescription channel #featureData: none #experimentData: use 'experimentData(object)' #Annotation: pd.hugene.1.0.st.v1 affyHebe #GeneFeatureSet (storageMode: lockedEnvironment) #assayData: 1102500 features, 20 samples #  element names: exprs #protocolData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: exprs dates #  varMetadata: labelDescription channel #phenoData #  rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 #    total) #  varLabels: index #  varMetadata: labelDescription channel #featureData: none #experimentData: use 'experimentData(object)' #Annotation: pd.hugene.1.0.st.v1 exprs(geneCore)[1:10,1:5]         01_A375_1.CEL 02_A375_2.CEL 03_A375_3.CEL 04_A375_4.CEL 7892501      6.909749      6.704112      6.633892      5.623717 7892502      4.980227      5.060804      4.231136      5.497120 7892503      3.321211      3.455340      3.180318      3.101057 7892504      8.177332      8.396480      8.088483      7.174240 7892505      4.767836      2.304351      2.578770      2.981808 7892506      4.932023      3.743294      3.011116      4.812062 7892507      4.123462      4.653984      4.799297      5.116502 7892508      5.006965      4.052511      2.868678      4.766367 7892509     12.518170     12.537957     12.475540     12.617325 7892510      4.830203      4.941844      3.543789      3.184837         05_A375_PCDNA3_1.CEL 7892501             5.831844 7892502             4.146510 7892503             3.149834 7892504             8.190490 7892505             2.751765 7892506             4.160108 7892507             4.888359 7892508             5.200189 7892509            12.517495 7892510             3.698457 exprs(genePS)[1:10,1:5]         01_A375_1.CEL 02_A375_2.CEL 03_A375_3.CEL 04_A375_4.CEL 7892501      6.909749      6.704112      6.633892      5.623717 7892502      4.980227      5.060804      4.231136      5.497120 7892503      3.321211      3.455340      3.180318      3.101057 7892504      8.177332      8.396480      8.088483      7.174240 7892505      4.767836      2.304351      2.578770      2.981808 7892506      4.932023      3.743294      3.011116      4.812062 7892507      4.123462      4.653984      4.799297      5.116502 7892508      5.006965      4.052511      2.868678      4.766367 7892509     12.518170     12.537957     12.475540     12.617325 7892510      4.830203      4.941844      3.543789      3.184837         05_A375_PCDNA3_1.CEL 7892501             5.831844 7892502             4.146510 7892503             3.149834 7892504             8.190490 7892505             2.751765 7892506             4.160108 7892507             4.888359 7892508             5.200189 7892509            12.517495 7892510             3.698457 [[alternative HTML version deleted]]

You only compared the 10 first units (which I'm pretty sure correspond to some controls). Note that your genePS has 257430 features, while geneCore has 33297. Get some other (random) probes and it's likely you're going to see the differences :) The 'probeset'-level data is what you're looking for. However, currently, oligo does not offer an alternative splicing tool, which will require you to find other tools after using oligo to preprocess your data. HTH, b On 21 September 2011 21:56, Irene Iba?ez <irenuliz at="" yahoo.com=""> wrote: > Dear list: > > I am performing microarray data analysis for the first time, so please forgive me if my question is inapproppriate. > I am analyzing Affymetrix GeneChip Human Gene 1.0 ST Array and I found that "oligo package" is ideal for this type of arrays. Then, I used it and I started with the guide of V5ExonGene.pdf that I downloaded from Bioconductor. > I read a post from the Bioconductor Mailing List Archives that said that "probeset" and "core" map to exons and genes respectively. Thus, why core expression and probeset expression showed the same values? > I checked mappedkeys and they are different for both of them. > Moreover I would like to study at the exon level my data, for that kind of analysis is it right to use the probeset expression? > > Thanks in advance. > > Ire > > P.S. Here is my code: > > #R version 2.12.0 (2010-10-15) > #Platform: x86_64-unknown-linux-gnu (64-bit) > > ### Libraries > library("affy") > library("limma") > #library("hugene10stv1cdf") > library("oligo") > library("hugene10stprobeset.db") > library("hugene10sttranscriptcluster.db") > library("pd.hugene.1.0.st.v1") > library("pd.hugene.1.1.st.v1") > library("IRanges") > library("affxparser") > library("Biobase") > library("preprocessCore") > library("Biostrings") > > #================================================== > ### Open files and read files > > archCEL<-list.celfiles("~/Documents/MICROARRAYS/MicroarraysHebeData_ Analisis/rawData", full.names=TRUE) > affyHebe <- read.celfiles(archCEL) > > #================================================== > ### rma > > genePS<-rma(affyHebe, target="probeset") > > geneCore<-rma(affyHebe, target="core") > > genePS > #ExpressionSet (storageMode: lockedEnvironment) > #assayData: 257430 features, 20 samples > #? element names: exprs > #protocolData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: exprs dates > #? varMetadata: labelDescription channel > #phenoData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: index > #? varMetadata: labelDescription channel > #featureData: none > #experimentData: use 'experimentData(object)' > #Annotation: pd.hugene.1.0.st.v1 > > geneCore > #ExpressionSet (storageMode: lockedEnvironment) > #assayData: 33297 features, 20 samples > #? element names: exprs > #protocolData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: exprs dates > #? varMetadata: labelDescription channel > #phenoData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: index > #? varMetadata: labelDescription channel > #featureData: none > #experimentData: use 'experimentData(object)' > #Annotation: pd.hugene.1.0.st.v1 > > affyHebe > #GeneFeatureSet (storageMode: lockedEnvironment) > #assayData: 1102500 features, 20 samples > #? element names: exprs > #protocolData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: exprs dates > #? varMetadata: labelDescription channel > #phenoData > #? rowNames: 01_A375_1.CEL 02_A375_2.CEL ... 20_A375_CAT_4.CEL (20 > #??? total) > #? varLabels: index > #? varMetadata: labelDescription channel > #featureData: none > #experimentData: use 'experimentData(object)' > #Annotation: pd.hugene.1.0.st.v1 > > exprs(geneCore)[1:10,1:5] > ??????? 01_A375_1.CEL 02_A375_2.CEL 03_A375_3.CEL 04_A375_4.CEL > 7892501????? 6.909749????? 6.704112????? 6.633892????? 5.623717 > 7892502????? 4.980227????? 5.060804????? 4.231136????? 5.497120 > 7892503????? 3.321211????? 3.455340????? 3.180318????? 3.101057 > 7892504????? 8.177332????? 8.396480????? 8.088483????? 7.174240 > 7892505????? 4.767836????? 2.304351????? 2.578770????? 2.981808 > 7892506????? 4.932023????? 3.743294????? 3.011116????? 4.812062 > 7892507????? 4.123462????? 4.653984????? 4.799297????? 5.116502 > 7892508????? 5.006965????? 4.052511????? 2.868678????? 4.766367 > 7892509???? 12.518170???? 12.537957???? 12.475540???? 12.617325 > 7892510????? 4.830203????? 4.941844????? 3.543789????? 3.184837 > ??????? 05_A375_PCDNA3_1.CEL > 7892501???????????? 5.831844 > 7892502???????????? 4.146510 > 7892503???????????? 3.149834 > 7892504???????????? 8.190490 > 7892505???????????? 2.751765 > 7892506???????????? 4.160108 > 7892507???????????? 4.888359 > 7892508???????????? 5.200189 > 7892509??????????? 12.517495 > 7892510???????????? 3.698457 > > > exprs(genePS)[1:10,1:5] > ??????? 01_A375_1.CEL 02_A375_2.CEL 03_A375_3.CEL 04_A375_4.CEL > 7892501????? 6.909749????? 6.704112????? 6.633892????? 5.623717 > 7892502????? 4.980227????? 5.060804????? 4.231136????? 5.497120 > 7892503????? 3.321211????? 3.455340????? 3.180318????? 3.101057 > 7892504????? 8.177332????? 8.396480????? 8.088483????? 7.174240 > 7892505????? 4.767836????? 2.304351????? 2.578770????? 2.981808 > 7892506????? 4.932023????? 3.743294????? 3.011116????? 4.812062 > 7892507????? 4.123462????? 4.653984????? 4.799297????? 5.116502 > 7892508????? 5.006965????? 4.052511????? 2.868678????? 4.766367 > 7892509???? 12.518170???? 12.537957???? 12.475540???? 12.617325 > 7892510????? 4.830203????? 4.941844????? 3.543789????? 3.184837 > ??????? 05_A375_PCDNA3_1.CEL > 7892501???????????? 5.831844 > 7892502???????????? 4.146510 > 7892503???????????? 3.149834 > 7892504???????????? 8.190490 > 7892505???????????? 2.751765 > 7892506???????????? 4.160108 > 7892507???????????? 4.888359 > 7892508???????????? 5.200189 > 7892509??????????? 12.517495 > 7892510???????????? 3.698457 > > > > ? ? ? ?[[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 > -- Successful people ask better questions, and as a result, they get better answers. 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