At the moment I do not know which steps you mean, but in principle, yes: After preprocessing you have a dataframe of gene expression levels. It does not matter which chip was used. Christian On 4/28/11 11:10 PM, Javier P?rez Florido wrote: > Thanks Christian, > But are also correct the other filter steps (the ones applied to 3'IVT) > for Gene arrays? > > Thanks, > Javier > > > On 28/04/2011 23:05, cstrato wrote: >> Dear Javier, >> >> In principle every workflow for Exon arrays can also be applied to >> Gene arrays. >> >> One more note: >> In principle you could use package "xps" for all these steps: >> >> - rma(.., exonlevel="core") will only use the core genes but not AFFX >> or control genes >> >> - PreFilter(mad=c(0.5,0.01)) etc will eliminate all transcripts with >> low variability >> >> For more details see e.g. example script "xps/examples/script4exon.R" >> which shows you the workflow for HuExon and HuGene 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 4/28/11 7:50 PM, Javier P?rez Florido wrote: >>> Dear list, >>> A possible data analysis workflow for EXON arrays could be as follows >>> (extracted from "Exon Array data analysis using Affymetrix Power Tools >>> and R statistical software", Briefings in Bioinformatics): >>> >>> * Normalization and summarization (at exon or gene-level) of the >>> array set. >>> * Quality control of exon array data of summarization results (to >>> remove possible outliers) >>> * Specific filtering steps, for example: >>> o Restrict analysis to core probesets >>> o Filter for undetected probesets (i.e., undetected exons), >>> making use of DABG (Detected above background) analysis. >>> o Filter for cross-hybridizing probesets (exons) >>> o Filter for genes undetected genes in all groups >>> >>> I'm running a gene-level data analysis on Human GENE ST 1.0 (not EXON) >>> arrays, which are, in principle, designed for gene expression profiling, >>> that is, a gene-level analysis. My question is related to the filtering >>> step. I was wondering if, once the normalization and summarization is >>> run at the transcript level (core), giving 33297 transcripts, the >>> following filtering can be run before differential expression analysis: >>> >>> * Remove control transcripts such as other_spike, AFFX, pos_control >>> (normgene->exon) and neg_control (normgene->intron). This step >>> removes around 4156 transcripts >>> * Remove transcripts with very low variability through varFilter >>> function (genefilter package) >>> >>> Since these were the steps recommended in "Bioconductor case studies" >>> book for 3'IVT arrays (the controls were different in 3'IVT), I was >>> wondering if these 2 filtering steps can also be used on Human Gene >>> arrays for gene-level analysis or, on the contrary, I have to run the >>> filtering steps described above for EXON arrays. >>> Thanks, >>> Javier >>> P.S. If you know any data analysis workflow document for HuGene arrays, >>> please, let me know >>> >>> [[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 >>> >> > >