On 17 March 2010 16:51, Sean Davis <seandavi at="" gmail.com=""> wrote: > 2010/3/17 Vincent Carey <stvjc at="" channing.harvard.edu="">: >> do you really want to put sample-characteristics data in a CEL file? >> >> the sample characteristics are available as follows: >> >> ?ff = getGEO("GSE4045") >> >>> table(pData(ff[[1]])$descr) >> >> ? ? ? ?conventional colorectal tumor, mucinous, Dukes Stage c, MSS, >> no cancer in the family, male, Distal Location , Tumor Grade 2 >> >> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 >> ?conventional colorectal tumor, non-mucinous, Dukes Stage b, MSS, no >> cancer in the family, female, Distal Location , Tumor Grade 2 >> >> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 >> conventional colorectal tumor, non-mucinous, Dukes Stage c, MSI, no >> cancer in the family, female, Proximal Location , Tumor Grade 3 >> >> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 >> .... >> >> and you will have to parse that 'description' field to extract stage >> and other relevant information. ?for example >> >> de = as.character(ff[[1]]$desc >> gr = gsub(".*, Tumor Grade.(.)$", "\\1", de) >> >> gives you a single character string for grade, except for sample 14 -- >> where my regexp doesn't do as much as it should. >> >> such activities would be used to populate an annotated data frame >> which could then serve as the phenoData component of an AffyBatch >> instance, which is a typical container for CEL-based intensity data, >> to be propagated downstream through background correction and >> normalization and so forth. ?The experimentData element should also be >> suitably populated, as early in the workflow as possible. ?If we look >> closely enough we can find that the ExpressionSet returned by getGEO >> has quantifications generated by MAS 5.0. >> >> On Wed, Mar 17, 2010 at 11:27 AM, ? ?? <greengarden_0925 at="" hotmail.com=""> wrote: >>> >>> >>> Hi: >>> >>> I've download ?the GSE CEL files from GEO. But I have trouble in adding the individual charateristics, such as tumor site, age, gender...and so on, to the CEL file. >>> >>> I've read the mail of [BioC] getGEO - getting the .CEL files from GEO,but still not understood. >>> >>> Could you use GSE4045 as an example to demonstrate >>> how to use the exprs(), I can find the instrucion in the mailing list, to replace the GSE4045.SOFT ?with the CEL raw microarray data and keep the characteristics left. >>> > > There are a couple of tricks here that can sometimes be useful to get > better annotation. ?In this case, they are not a big improvement. > > The GEO GSE data entity contains information as supplied by the > submitters. ?The GDS data entity contains data taken from GSE records > that have been further curated by GEO staff. ?Often, that leads to > more useful annotation than comma-separated lists (although the > information is usually the same or similar, at least). ?To give an > example of how one might learn of the existence of such a GDS given a > GSE, one can use the GEOmetadb package: > > library(GEOmetadb) > # Next command will take a minute.... > sqlfile = getSQLiteFile() > # Check to see if the GSE record has a corresponding > # GDS record > geoConvert('GSE4045','gds') > > This series of commands will result in the following: > > $gds > ?from_acc ?to_acc > 1 ?GSE4045 GDS2201 > > So, GSE4045 has been curated by NCBI GEO staff and the accession of > the curated data is GDS2201. ?We can check to see what subsets > (phenotypic variables) are available using GEOmetadb, but we have to > resort to writing SQL to do so: > > # make a connection to the database > conn = dbConnect('SQLite',sqlfile) > dbGetQuery(conn,"select > gds_subset.gds,gds_subset.description,gds_subset.type from gds_subset > where gds='GDS2201'") > > One can use the columnDescriptions() function to get a data.frame of > columns, tables, and descriptions if writing SQL is necessary. ?This > will return this small data.frame: > > ? ? ?gds ? ? ? ? ? ? ? ? ? ? ? description ? ? ? ? ?type > 1 GDS2201 ? ? serrated colerectal carcinoma disease state > 2 GDS2201 conventional colorectal carcinoma disease state > > So, unfortunately, the GEO staff has annotated only the two different > types of colorectal carcinoma and not the other clinical variables. > If this is all you wanted, then you can use getGEO('GDS2201') to get > the annotations and attach those to the ExpressionSet that you create > by normalizing the .CEL files of your choosing. ?If not, then Vince's > method is the way to go. > > Sean > It's also worth noting that ArrayExpress have imported much of the data from common Affymetrix platforms (and some other platforms) from GEO. These imported data sets have generally been put through a basic curation step which does improve the computability of the annotation somewhat. The general rule is that for a GEO series GSENNNN then the ArrayExpress entry is E-GEOD-NNNN: library(ArrayExpress) abatch <- ArrayExpress('E-GEOD-4045') Not that it makes a huge difference in this case, but this is a pretty good workaround when a GDS set is not available in GEO. Cheers, Tim -- (former AE curator) Bioinformatician, Smith Lab CIMR, University of Cambridge