Dear all, I'd appreciate your input if you could. I first normalized data, and it is called john2. We are interested in Notch signaling pathway and want to get the expression values for all genes on this pathway. So I tried the below (it does not look neat...sorry that I am new to R coding): ######KEGG:Notch##################### ######http://www.genome.ad.jp/kegg/pathway/hsa/hsa04330.html###### Notch<-mget("04330",lumiHumanAllPATH2PROBE,ifnotfound = NA) write.table(Notch, file="Notch.txt",quote=FALSE,row.names=FALSE,col.names = FALSE) Notch1<-read.table('Notch.txt',header=FALSE) Notch_geneSymbol <- getSYMBOL(as.vector(Notch1[,1]), 'lumiHumanAll.db') ########Retrieve the normalized data######### dataMatrix <- exprs(john2) probeList <- rownames(dataMatrix) geneSymbol <- getSYMBOL(probeList, 'lumiHumanAll.db') anno<-data.frame(dataMatrix,geneSymbol=geneSymbol) ########Subset expression data for Notch genes####### b<-anno[as.vector(Notch1[,1]),] dim(b) You will see many empty rows in b filled with "NA". I wonder what the reason could be. Thank you so much! Allen > b[1:10,1:3] X451Lu X451Lu885 geneSymbol NA NA NA <na> QeUV54t73rDarMMp3k 9.220161 9.577290 JAG1 NqbdUs8V6OMoPh1puQ 10.386946 10.960459 CREBBP Te17_XV.gA5S4QmAUE 8.250515 8.325535 CREBBP KghpNGCSdQCgCfqa5U 10.038218 9.930820 CTBP1 TnYhDwobqOKdYhKGa8 9.509115 9.382045 CTBP1 NA.1 NA NA <na> iAW0JWKaMiFGUuFL7I 8.724987 8.597907 CTBP1 NA.2 NA NA <na> NA.3 NA NA <na> > Notch_geneSymbol[1:10] QTei90CF6p57gnoHmo QeUV54t73rDarMMp3k NqbdUs8V6OMoPh1puQ Te17_XV.gA5S4QmAUE "JAG1" "JAG1" "CREBBP" "CREBBP" KghpNGCSdQCgCfqa5U TnYhDwobqOKdYhKGa8 f2IQ8KG6jinWIShmvc iAW0JWKaMiFGUuFL7I "CTBP1" "CTBP1" "CTBP1" "CTBP1" NpkSqRU027UU5_RSBQ K3FQQYgKWgrnmnnTRo "CTBP2" "DTX1" > [[alternative HTML version deleted]]

Hi Allen -- ss <affysnp at="" gmail.com=""> writes: > Dear all, > I'd appreciate your input if you could. > I first normalized data, and it is called john2. We are interested in Notch > signaling > pathway and want to get the expression values for all genes on this pathway. > So > I tried the below (it does not look neat...sorry that I am new to R coding): Here's a simple alternative... library(lumi) data(example.lumi) annotation(example.lumi) <- "lumiHumanAll" the last line above is a hack, the annotation package 'lumiHumanAllV1" is not available to me, and the rest of the steps require a valid annotation package. It's worth getting the LumiBatch right up front, so you can rely on the software to do things like subsetting and keeping track of the annotation information for you. Now get the relevant probe IDs, select just those that are in the LumiBatch, and subset pid <- toTable(lumiHumanAllPATH2PROBE["04330"])$probe_id ok_pid <- pid[pid %in% featureNames(example.lumi)] notchSet <- example.lumi[ok_pid,] You now have a mini-LumiBatch that you can manipulate any way you like, e.g., > exprs(notchSet) A01 A02 B01 B02 NqbdUs8V6OMoPh1puQ 1854.2 2004.0 1787.4 1728.9 ElXciyeulKjgX5Ulus 506.6 522.9 428.4 502.7 KUXd9XWh3ASgfgx6iI 185.0 288.8 164.6 258.6 Npzx7j5NWp6CpNB5TM 115.2 123.8 108.2 104.3 B.VJf357yqcU6hSd6U 1175.6 1560.4 1449.6 1362.0 NrAl0kElLoh6BEFMl4 253.1 275.1 212.7 201.9 0TVfhL5Jd_F2jBDCIk 3892.0 5297.3 4867.7 4043.8 Teee5.43efFvQim7ko 1222.2 1908.2 1713.1 1485.6 fooKDKRK3dQLg4HfFQ 1753.2 1911.4 1751.7 1732.7 0niXHt19Ue1.dSldI4 2280.6 3742.3 2651.0 3072.9 ZkeX1uG2W_RzoS_d8I 204.8 241.2 189.8 211.4 3CuC6Kue64npR1Inks 1082.0 1379.7 1068.1 1212.2 Hylf6kIO1txWn1FFd4 155.7 175.0 153.6 164.0 iNFN9SNxaVZ6rlfqhw 1132.2 1380.5 1227.5 1184.4 KRUFAvUqf9CgeFEkuE 185.2 215.0 179.5 188.3 xaiVLOqoKt8XSS8VR8 294.9 320.5 325.3 245.0 cXu3S3W0t7f3Wd93hU 1873.4 2201.1 2042.0 1692.0 > syms <- getSYMBOL(featureNames(notchSet), annotation(notchSet)) > data.frame(exprs(notchSet), GeneSymbol=syms) A01 A02 B01 B02 GeneSymbol NqbdUs8V6OMoPh1puQ 1854.2 2004.0 1787.4 1728.9 CREBBP ElXciyeulKjgX5Ulus 506.6 522.9 428.4 502.7 KAT2A KUXd9XWh3ASgfgx6iI 185.0 288.8 164.6 258.6 HES1 Npzx7j5NWp6CpNB5TM 115.2 123.8 108.2 104.3 JAG2 B.VJf357yqcU6hSd6U 1175.6 1560.4 1449.6 1362.0 MFNG NrAl0kElLoh6BEFMl4 253.1 275.1 212.7 201.9 PSEN1 0TVfhL5Jd_F2jBDCIk 3892.0 5297.3 4867.7 4043.8 NUMB Teee5.43efFvQim7ko 1222.2 1908.2 1713.1 1485.6 NCOR2 fooKDKRK3dQLg4HfFQ 1753.2 1911.4 1751.7 1732.7 SNW1 0niXHt19Ue1.dSldI4 2280.6 3742.3 2651.0 3072.9 NCSTN ZkeX1uG2W_RzoS_d8I 204.8 241.2 189.8 211.4 DLL1 3CuC6Kue64npR1Inks 1082.0 1379.7 1068.1 1212.2 APH1A Hylf6kIO1txWn1FFd4 155.7 175.0 153.6 164.0 DLL4 iNFN9SNxaVZ6rlfqhw 1132.2 1380.5 1227.5 1184.4 PSENEN KRUFAvUqf9CgeFEkuE 185.2 215.0 179.5 188.3 MAML2 xaiVLOqoKt8XSS8VR8 294.9 320.5 325.3 245.0 PTCRA cXu3S3W0t7f3Wd93hU 1873.4 2201.1 2042.0 1692.0 DTX3 And another way, a little twisted... Load some packages and a sample data set library(GSEABase) Then create a GeneSetCollection, in general one for each KEGG id, but for you a collection of one gsc <- GeneSetCollection(example.lumi, setType=KEGGCollection("04330")) And then use the first (and only!) gene set in the collection to subset example.lumi notchSet <- example.lumi[gsc[[1]],] Martin > ######KEGG:Notch##################### > ######http://www.genome.ad.jp/kegg/pathway/hsa/hsa04330.html###### > Notch<-mget("04330",lumiHumanAllPATH2PROBE,ifnotfound = NA) > write.table(Notch, file="Notch.txt",quote=FALSE,row.names=FALSE,col.names = > FALSE) > Notch1<-read.table('Notch.txt',header=FALSE) > Notch_geneSymbol <- getSYMBOL(as.vector(Notch1[,1]), 'lumiHumanAll.db') > > ########Retrieve the normalized data######### > dataMatrix <- exprs(john2) > probeList <- rownames(dataMatrix) > geneSymbol <- getSYMBOL(probeList, 'lumiHumanAll.db') > anno<-data.frame(dataMatrix,geneSymbol=geneSymbol) > > ########Subset expression data for Notch genes####### > b<-anno[as.vector(Notch1[,1]),] > dim(b) > > > You will see many empty rows in b filled with "NA". I wonder what > the reason could be. > > Thank you so much! > > > Allen > > >> b[1:10,1:3] > X451Lu X451Lu885 geneSymbol > NA NA NA <na> > QeUV54t73rDarMMp3k 9.220161 9.577290 JAG1 > NqbdUs8V6OMoPh1puQ 10.386946 10.960459 CREBBP > Te17_XV.gA5S4QmAUE 8.250515 8.325535 CREBBP > KghpNGCSdQCgCfqa5U 10.038218 9.930820 CTBP1 > TnYhDwobqOKdYhKGa8 9.509115 9.382045 CTBP1 > NA.1 NA NA <na> > iAW0JWKaMiFGUuFL7I 8.724987 8.597907 CTBP1 > NA.2 NA NA <na> > NA.3 NA NA <na> > >> Notch_geneSymbol[1:10] > QTei90CF6p57gnoHmo QeUV54t73rDarMMp3k NqbdUs8V6OMoPh1puQ Te17_XV.gA5S4QmAUE > "JAG1" "JAG1" "CREBBP" "CREBBP" > KghpNGCSdQCgCfqa5U TnYhDwobqOKdYhKGa8 f2IQ8KG6jinWIShmvc iAW0JWKaMiFGUuFL7I > "CTBP1" "CTBP1" "CTBP1" "CTBP1" > NpkSqRU027UU5_RSBQ K3FQQYgKWgrnmnnTRo > "CTBP2" "DTX1" >> > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- Martin Morgan Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. 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