after you obtain credentials from netaffx, you can do something like the following library(AffyCompatible) rsrc = NetAffxResource(user=[user], password=[pass]) lk = grep("Ex", names(rsrc), value=TRUE) names(rsrc[[lk[3]]]) fl = readAnnotation(rsrc, anno=rsrc[["HuEx-1_0-st-v2", "Transcript Cluster Annotations, CSV"]], content=FALSE) con = unz(fl, "HuEx-1_0-st-v2.na33.1.hg19.transcript.csv") smal = read.csv(con, skip=24, nrow=20, stringsAsFactors=FALSE) # drop the nrow for full # import smal[,1:7] > smal[,1:7] transcript_cluster_id probeset_id seqname strand start stop total_probes 1 2315100 2315100 chr1 + 11884 14409 20 2 2315106 2315106 chr1 + 14760 15198 8 3 2315109 2315109 chr1 + 19408 19712 4 4 2315113 2315113 chr1 + 27563 27813 4 5 2315115 2315115 chr1 + 28425 29158 4 6 2315117 2315117 chr1 + 30623 30667 4 7 2315119 2315119 chr1 + 35690 35804 4 8 2315121 2315121 chr1 + 35928 35968 2 9 2315125 2315125 chr1 + 51913 70633 26 10 2315129 2315129 chr1 + 52452 63887 20 11 2315145 2315145 chr1 + 115060 116054 4 12 2315147 2315147 chr1 + 131158 146731 44 13 2315160 2315160 chr1 + 136242 136557 8 14 2315163 2315163 chr1 + 138440 662350 85 15 2315189 2315189 chr1 + 140341 140737 4 16 2315191 2315191 chr1 + 141298 320283 15 17 2315198 2315198 chr1 + 148041 682717 20 18 2315206 2315206 chr1 + 158150 164721 8 19 2315209 2315209 chr1 + 236570 238386 8 20 2315212 2315212 chr1 + 237097 237462 4 substr(smal[,8], 1, 60) > substr(smal[,8], 1, 60) [1] "NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box hel" [2] "AK092583 // WASH7P // WAS protein family homolog 7 pseudogen" [3] "NR_033266 // WASH5P // WAS protein family homolog 5 pseudoge" [4] "---" [5] "ENST00000422679 // MIR1302-11 // microRNA 1302-11 // --- // " [6] "ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // " [7] "NR_026818 // FAM138A // family with sequence similarity 138," [8] "ENST00000448235 // FAM138F // family with sequence similarit" [9] "NM_001005240 // OR4F17 // olfactory receptor, family 4, subf" [10] "ENST00000492842 // OR4G11P // olfactory receptor, family 4, " [11] "---" [12] "ENST00000432723 // CICP7 // capicua homolog (Drosophila) pse" [13] "AK303004 // FLJ45445 // uncharacterized LOC399844 // 19p13.3" [14] "AK302487 // FLJ45340 // uncharacterized LOC402483 // 7q32.1 " [15] "NR_039983 // LOC729737 // uncharacterized LOC729737 // 1p36." [16] "ENST00000570230 // LOC100653348 // uncharacterized LOC100653" [17] "NR_039983 // LOC729737 // uncharacterized LOC729737 // 1p36." [18] "NR_029401 // LOC731275 // uncharacterized LOC731275 // 1q43 " [19] "ENST00000424587 // LOC100508047 // uncharacterized LOC100508" [20] "NR_029406 // FLJ43681 // ribosomal protein L23a pseudogene /" so it appears you have to parse the 8th column to get the symbols On Tue, Jan 28, 2014 at 5:11 AM, Vincent Carey <stvjc@channing.harvard.edu>wrote: > for the record, my reply goes down the wrong path, to gene 1.0 st > annotation. > > it seems likely that the solution lies in AffyCompatible package, and that > resource should > be mentioned in > > http://www.bioconductor.org/help/workflows/arrays/ > > > On Tue, Jan 28, 2014 at 4:41 AM, Vincent Carey <stvjc@channing.harvard.edu> > wrote: > >> I am no expert on the vocabulary here, but this shows two ways of working >> with the ChipDb package. First we use some SQL to figure out what is in >> there >> >> > library(hugene10sttranscriptcluster.db) >> >> > con = hugene10sttranscriptcluster_dbconn() # do dbListTables(con) too >> >> > dbGetQuery(con, "select * from probes limit 5") >> probe_id gene_id is_multiple >> 1 7892501 <na> 0 >> 2 7892502 <na> 0 >> 3 7892503 <na> 0 >> 4 7892504 <na> 0 >> 5 7892505 <na> 0 >> >> > peek = dbGetQuery(con, "select * from probes where gene_id is not null >> limit 5") >> >> > peek >> probe_id gene_id is_multiple >> 1 7896740 81099 0 >> 2 7896742 346288 0 >> 3 7896744 81399 0 >> 4 7896754 100287934 0 >> 5 7896756 157693 0 >> >> # so now we know that there is some attribute 'probe_id' that maps to >> something >> # called gene_id. now let's use the select interface >> >> > somekeys = peek[,1] >> >> > select( hugene10sttranscriptcluster.db, keytype="PROBEID", >> keys=somekeys, columns=c("SYMBOL", "ENTREZID")) >> PROBEID SYMBOL ENTREZID >> 1 7896740 OR4F17 81099 >> 2 7896742 SEPT14 346288 >> 3 7896744 OR4F16 81399 >> 4 7896754 LOC100287934 100287934 >> 5 7896756 FAM87A 157693 >> >> >> > sessionInfo() >> R Under development (unstable) (2013-12-01 r64371) >> Platform: x86_64-apple-darwin10.8.0/x86_64 (64-bit) >> >> locale: >> [1] >> en_US.US-ASCII/en_US.US-ASCII/en_US.US-ASCII/C/en_US.US-ASCII/en_US .US-ASCII >> >> attached base packages: >> [1] parallel stats graphics grDevices datasets utils tools >> [8] methods base >> >> other attached packages: >> [1] hugene10sttranscriptcluster.db_8.0.1 org.Hs.eg.db_2.10.1 >> >> [3] RSQLite_0.11.4 DBI_0.2-7 >> >> [5] AnnotationDbi_1.25.9 Biobase_2.23.3 >> >> [7] BiocGenerics_0.9.3 BiocInstaller_1.13.3 >> >> [9] weaver_1.29.1 codetools_0.2-8 >> >> [11] digest_0.6.4 >> >> loaded via a namespace (and not attached): >> [1] IRanges_1.21.21 stats4_3.1.0 >> >> >> >> On Tue, Jan 28, 2014 at 4:18 AM, Stephane Plaisance | VIB | < >> stephane.plaisance@vib.be> wrote: >> >>> I am looking for a file or package that can map my transcript_cluster_id >>> to hgnc symbols (human) >>> my data was preprocessed using APT and I do not have the original probe >>> IDs. >>> >>> Apparently, only 'probeIDs' are supported by BiomaRt, not >>> 'transcript_cluster_id'. >>> I tried hugene10sttranscriptcluster.db" but the IDs are different >>> (probeIDs) >>> >>> Where can I find a table listing 'transcript_cluster_id' and enough info >>> to get symbols? >>> >>> thanks in advance >>> Stephane >>> >>> >>> >>> [[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 >>> >> >> > [[alternative HTML version deleted]]