Hi Lesley, On 10/3/2012 2:29 PM, Hoyles, Lesley wrote: > Hi Jim > > Thanks, the reannotation worked a treat. I've been able to export the normalized data in annotated format. > > I am adverse to removing probes that have no Entrez ID associated with them as I want to put the whole set of data through limma. I can't use the annotated expr.loess in lmFit, but is there a way I can get the symbol information into the output of lmFit (for instance, as fit$symbol)? There is a 'genes' slot to an MArrayLM object (the output from e.g., lmFit) into which you can stuff a data.frame containing gene symbols, etc. Another option is to use the annaffy package to do the annotation. And if you are going to use annaffy and limma, then I should make a shameless plug for the affycoretools package, which contains a function designed to go from an MArrayLM object to annotated output in a single function call (outputting HTML or text files). Best, Jim > > Best wishes > Lesley > . > > ________________________________________ > From: James W. MacDonald [jmacdon at uw.edu] > Sent: 03 October 2012 16:30 > To: Hoyles, Lesley > Cc: bioconductor at stat.math.ethz.ch > Subject: Re: [BioC] Assigning gene symbols to Affymetrix data and averaging probes > > Hi Lesley, > > On 10/3/2012 10:55 AM, Hoyles, Lesley wrote: >> Hi >> >> I have processed my affy data and am able to annotate the object >> mice.loess using the following. ID<- featureNames(mice.loess) Symbol >> <- getSYMBOL(ID,'mouse4302.db') fData(mice.loess)<- >> data.frame(ID=ID,Symbol=Symbol) >> >> >> However, when I convert my object as follows - expr.loess<- >> exprs(mice.loess) - I lose the annotation and have been unable to >> find a way to annotate expr.loess. Please could anybody suggest how I >> can annotate expr.loess? > expr.loess<- data.frame(ID = ID, Symbol = Symbol, exprs(mice.loess)) > >> >> Is there a way of averaging probes for each gene with Affymetrix >> data? I've been able to do this with single-channel Agilent data >> using the example given in the limma guide. > There are probably two reasonable ways to do this. First, the easiest. > > dat<- ReadAffy(cdfname = "mouse4302mmentrezcdf") > > and proceed from there. This will use the MBNI re-mapped CDF package > based on Entrez Gene IDs, and you will have a single value per gene > after summarization. There are other ways to map the probes; see > http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/C DF_download.asp > at the bottom of the page for more info. > > Alternatively if you want to stick with the original probesets, the > problem arises that some probesets are not well annotated, so what to do > with those? In addition, gene symbols are not guaranteed to be unique, > so you can't just assume that they are. Entrez Gene and UniGene IDs are > supposed to be unique, so you could go with them, doing something like > (untested) > > gns<- toTable(mouse4302ENTREZID) > alldat<- merge(gns, expr.loess, by = 1) ## where expr.loess is the > data.frame I suggest above > alldatlst<- tapply(1:nrow(alldat), alldat$gene_id, function(x) alldat[x,]) > combined.data<- do.call("rbind", lapply(alldatlst, function(x) > c(x[1,1:3], colMeans(x[,-c(1:3)]))) > > Here I am assuming that after the merge() step the first three columns > are the probeset ID, gene_id, symbol, and the remaining columns are the > expression values. You will lose all data for which there isn't an > Entrez Gene ID, but the same is true of the MBNI method I outline above. > > Best, > > Jim > > >> >> Thanks in advance for your help. >> >> Best wishes Lesley _______________________________________________ >> 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 > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099