Dear list, I am quite new to R as well as to microarray analysis. I am dealing with some gene expression analysis performed on Affymetrix Human Gene 1.0ST microarray. So far, I have learnt how to filtrate data using genefilter using nsFilter functions. Now, I would like to know how to filter out from the expressionset object all the control probesets (~4000) that Affymetrix includes in the microarray (for quality assay, normalization, background correction, etc.). However, none of the aforementioned functions worked for me. How can I recognize those probesets and remove them? I would like to filter them out before statistical analysis with limma package. Thank you very much in advance for your help. Virginia.

Hi Virginia, On 6/21/2011 6:46 AM, Virginia Garcia wrote: > Dear list, > > I am quite new to R as well as to microarray analysis. > I am dealing with some gene expression analysis performed on Affymetrix Human > Gene 1.0ST microarray. > > So far, I have learnt how to filtrate data using genefilter using nsFilter > functions. > > Now, I would like to know how to filter out from the expressionset object all > the control probesets (~4000) that Affymetrix includes in the microarray (for > quality assay, normalization, background correction, etc.). However, none of > the aforementioned functions worked for me. > > How can I recognize those probesets and remove them? I would like to filter > them out before statistical analysis with limma package. How much do you like database stuff? Lots? Great, I have some fun for you. Assuming you have pd.hugene.1.0.st.v1 installed (I have 1.1 installed, but the queries will be the same). > library(pd.hugene.1.1.st.v1) First, get a connection to the database > con <- db(pd.hugene.1.1.st.v1) Now, what's in this thing? > dbListTables(con) [1] "chrom_dict" "core_mps" "featureSet" "level_dict" "pmfeature" [6] "table_info" "type_dict" OK, let's dig. > dbGetQuery(con, "select * from pmfeature limit 5;") fid fsetid atom x y 1 704656 7892501 1 765 711 2 1060101 7892501 2 800 1070 3 1046459 7892501 3 28 1057 4 403586 7892501 4 655 407 5 473527 7892502 5 306 478 Boring. > dbGetQuery(con, "select * from featureSet limit 5;") fsetid strand start stop transcript_cluster_id exon_id crosshyb_type level 1 7892501 NA 0 0 0 0 0 NA 2 7892502 NA 0 0 0 0 0 NA 3 7892503 NA 0 0 0 0 0 NA 4 7892504 NA 0 0 0 0 0 NA 5 7892505 NA 0 0 0 0 0 NA chrom type 1 NA 6 2 NA 7 3 NA 7 4 NA 7 5 NA 7 Maybe more interesting. What's this 'type' business? > dbGetQuery(con, "select * from type_dict limit 5;") type type_id 1 1 main 2 2 control->affx 3 3 control->chip 4 4 control->bgp->antigenomic 5 5 control->bgp->genomic Now that looks like some reasonable info. What different types are there? > dbGetQuery(con, "select * from type_dict;") type type_id 1 1 main 2 2 control->affx 3 3 control->chip 4 4 control->bgp->antigenomic 5 5 control->bgp->genomic 6 6 normgene->exon 7 7 normgene->intron 8 8 rescue->FLmRNA->unmapped So it looks like pretty much everything but type 1 are controls of some type. > tab <- dbGetQuery(con, "select * from featureSet;") > table(tab$type) 1 2 4 6 7 253002 57 45 1195 2904 So that's about 4200 control probes (2,4,6,7). How to subset from here depends on the package you are using for analysis (oligo, affy, xps), so I won't go into that. But you can now get the IDs of the probesets you care about and use them to filter. Best, Jim > > Thank you very much in advance for your help. > > Virginia. > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues