Dear Javier, I would suggest to look at the Affymetrix annotation file "HuGene-1_0-st-v1.na31.hg19.probeset.csv". There you can see the following for e.g. SAMD11: transcript_cluster_id = 7896761 number of probesets for 7896761 is 17 number of probes per probeset is 2 (with 2 exceptions) Then you can open "HuEx-1_0-st-v2.na31.hg19.probeset.csv" and compare the data for yourself. Best regards Christian On 4/26/11 11:13 AM, Javier P?rez Florido wrote: > Dear Christian, > Thanks for your reply, but, is it right to assert that, for Gene arrays, > probesets target a particular exon of a particular gene and transcript > cluster enables gene-level estimates as Exon arrays, but using less > probes per exon? > > Thanks, > Javier > > > On 25/04/2011 21:13, cstrato wrote: >> While Exon ST arrays have usually 4 probes per probeset, Gene ST >> arrays have only 1-2 probes per probeset. Thus my personal opinion is >> not to use Gene ST arrays to detect alternative splicing events. >> >> However, there exists e.g. FIRMAGene for this purpose, see: >> http://bioinf.wehi.edu.au/folders/firmagene/ >> >> Best regards >> Christian >> >> >> On 4/25/11 8:54 PM, Javier P?rez Florido wrote: >>> Sorry, I always forget sessionInfo(), see below >>> >>> You are right, for Human Gene ST arrays and at transcript level, only >>> "core" mode exists. However, when: >>> fit<-fitPLM(OligoRaw) >>> where OligoRaw is the set of Raw data, the size of "fit" object is >>> 257,430 and when the following command is executed >>> >>> OligoEset<-rma(OligoRaw,target="probeset") >>> >>> OligoEset has 257,430 features. So, the RMA procedure "inside" fitPLM >>> function performs a normalization at the probeset level. >>> >>> On the other hand, summarization using RMA can be performed at the >>> transcript level in the following way: >>> OligoEset<-rma(OligoRaw,target="core") >>> >>> which yields around 33000 transcripts. >>> >>> I'm still confused about the concepts of "probeset" and "transcript" on >>> Human Gene Arrays. >>> >>> For Exon arrays, probesets consists of four individual probes and >>> usually target a particular exon of a particular gene. Thus exon- level >>> intensity estimates correspond to the probeset-level estimates. >>> Probesets are further grouped into transcript clusters enabling >>> gene-level estimate to be computed by summarizing data from all probes >>> within the transcript cluster. >>> >>> However, I don't know if I can assert that, for Gene arrays, probesets >>> target a particular exon of a particular gene and transcript cluster >>> enables gene-level estimates as Exon arrays. The only difference is >>> that, for Exon arrays, we have two more "annotation levels" with less >>> confidence score (extended and full). Otherwise, what is the utility of >>> summarizing at the probeset level on Hu Gene arrays? >>> >>> This is related to my second question: can HuGene could detect >>> alternative splice events reliably? Can HuGene be used as an economical >>> exon array for just the well-annotated content (core)? >>> >>> Thanks again, >>> Javier >>> >>> >>> Thanks, >>> Javier >>> >>> >>> R version 2.13.0 (2011-04-13) >>> Platform: x86_64-pc-mingw32/x64 (64-bit) >>> >>> locale: >>> [1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252 >>> LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C >>> [5] LC_TIME=Spanish_Spain.1252 >>> >>> attached base packages: >>> [1] stats graphics grDevices utils datasets methods base >>> >>> other attached packages: >>> [1] pd.hugene.1.0.st.v1_3.0.2 hugene10sttranscriptcluster.db_7.0.1 >>> org.Hs.eg.db_2.5.0 RSQLite_0.9-4 >>> [5] DBI_0.2-5 AnnotationDbi_1.14.1 oligo_1.16.0 oligoClasses_1.14.0 >>> [9] affyPLM_1.28.5 preprocessCore_1.14.0 gcrma_2.24.1 affy_1.30.0 >>> [13] Biobase_2.12.1 >>> >>> loaded via a namespace (and not attached): >>> [1] affxparser_1.24.0 affyio_1.20.0 Biostrings_2.20.0 bit_1.1-6 ff_2.2-1 >>> IRanges_1.10.0 splines_2.13.0 tools_2.13.0 >>> >>> >>> >>> >>> On 25/04/2011 19:36, cstrato wrote: >>>> Dear Javier, >>>> >>>> Since you do not supply your sessionInfo() it is not possible to >>>> answer your question. >>>> >>>> However, please note that levels core, extended, full do only exist >>>> for Exon ST arrays but not for Gene ST arrays. >>>> >>>> Best regards >>>> Christian >>>> _._._._._._._._._._._._._._._._._._ >>>> C.h.r.i.s.t.i.a.n S.t.r.a.t.o.w.a >>>> V.i.e.n.n.a A.u.s.t.r.i.a >>>> e.m.a.i.l: cstrato at aon.at >>>> _._._._._._._._._._._._._._._._._._ >>>> >>>> >>>> On 4/25/11 7:24 PM, Javier P?rez Florido wrote: >>>>> Dear list, >>>>> I have two questions regarding Human Gene 1.0 ST arrays: >>>>> >>>>> * Both NUSE and RLE plots need a fitted object using fitPLM >>>>> function. Now, this function accepts raw data from a set of Hu >>>>> Gene 1.0 arrays, but, internally, this function performs a RMA >>>>> normalization. What level is used for this normalization? I cannot >>>>> choose the level (i.e. core, full, extended) for the "internal" >>>>> normalization. >>>>> * Are a splicing analysis using Hu Gene 1.0 arrays (core analysis) >>>>> and a splicing analysis using Hu Exon 1.0 arrays (core analysis) >>>>> equivalent in terms of results? >>>>> >>>>> >>>>> Thanks, >>>>> Javier >>>>> >>>>> >>>>> [[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 >>>>> >>>> >>> >>> >> > >