GeneFeatureSet
1
1
Entering edit mode
@shahenda-el-naggar-5683
Last seen 9.6 years ago
Hi I am trying to combine data that was generated from different microarray platforms (MoGene_10_st and mouse4302) when trying to filter genes using P/A calls, I ran into problems with MoGene since it does not have MM probes to run functions like mas5call instead I used Oligo package. The problem is when I run the function paCalls and then subset into normalized data I end up with control probes. To avoid that I am trying to remove control probes first then apply paCalls function, however, so far I could not find any function that allow me to do that with GeneFeatureSet which is initially generated through reading CEL files by oligo library this is my code library(oligo) library(pd.mogene.1.0.st.v1) geneCELs <- list.celfiles("C:\\Documents and Settings\\All Users\\Desktop\\Mouse data\\GSE37832_RAW\\MENSC", full.names = TRUE) t<-read.celfiles(geneCELs) probein<- getProbeInfo(t,  probeType = "pm", target = "probeset", sortBy = "none") p<-paCalls(t, method="DABG") indsum <- apply(p [,1:6], 1, function(x) sum(x[1:3] < 0.05) > 2 || sum(x[4:6] < 0.05) > 2) pfinal <- p[insum,] probesetids <- subset(probein, probein$fid %in% as.character(rownames(pfinal))) dataset.1.mensc <-subset(mendsc.eset, rownames(mensc.eset) %in% as.character(probesetids$man_fsetid))  #mensc.est is normalized using expresso function write.csv(dataset.1.mensc, "dataset.1.mensc.csv") dim(dataset.1.mensc) Shahenda [[alternative HTML version deleted]]
oligo oligo • 1.7k views
ADD COMMENT
1
Entering edit mode
@james-w-macdonald-5106
Last seen 33 minutes ago
United States
Hi Shahenda, On 1/6/2013 1:50 AM, Shahenda El-Naggar wrote: > Hi > I am trying to combine data that was generated from different microarray platforms (MoGene_10_st and mouse4302) > when trying to filter genes using P/A calls, I ran into problems with MoGene since it does not have MM probes to run functions like mas5call > instead I used Oligo package. The problem is when I run the function paCalls and then subset into normalized data I end up with control probes. To avoid that I am trying to remove control probes first then apply paCalls function, however, so far I could not find any function that allow me to do that with GeneFeatureSet which is initially generated through reading CEL files by oligo library > this is my code Removing the controls is fairly simple. I have a function in the devel version of affycoretools that you can use. Rather than installing the devel version (for which you should also install the devel version of R), you can just copy paste the following into your R session, and then use. getMainProbes <- function (input) { if (is(input, "ExpressionSet")) { pdinfo <- annotation(input) if (length(grep("^pd", pdinfo)) != 1) stop(paste("The file", pdinfo, "does not appear to have been processed using", "the oligo package.\nIn this case the argument to this function should", "be the name of the correct pd.info package (e.g., pd.hugene.1.0.st.v1.\n"), call. = FALSE) } else { if (is.character(input)) pdinfo <- input else if (!is.character(input)) stop(paste("The input argument for this function should either be an ExpressionSet", "that was generated using oligo, or the name of the pd.info package", "that corresponds to your data.\n"), call. = FALSE) } require(pdinfo, character.only = TRUE) con <- db(get(pdinfo)) types <- dbGetQuery(con, paste("select distinct meta_fsetid, type from featureSet inner join core_mps", "on featureSet.fsetid=core_mps.fsetid;")) ind <- types$type %in% 1 dbDisconnect(con) if (is(input, "ExpressionSet")) return(input[ind, ]) else return(ind) } Note that this is intended for use on summarized data. I don't see where you are summarizing the mogene expression data below, and quite frankly have no idea what you are trying to accomplish with that code. In general, I would do something like eset <- rma(t) eset <- getMainProbes(eset) and then eset would contain only probes with a 'main' designation. Best, Jim > > library(oligo) > library(pd.mogene.1.0.st.v1) > geneCELs<- list.celfiles("C:\\Documents and Settings\\All Users\\Desktop\\Mouse data\\GSE37832_RAW\\MENSC", full.names = TRUE) > t<-read.celfiles(geneCELs) > probein<- getProbeInfo(t, probeType = "pm", target = "probeset", sortBy = "none") > p<-paCalls(t, method="DABG") > indsum<- apply(p [,1:6], 1, function(x) sum(x[1:3]< 0.05)> 2 || sum(x[4:6]< 0.05)> 2) > pfinal<- p[insum,] > probesetids<- subset(probein, probein$fid %in% as.character(rownames(pfinal))) > dataset.1.mensc<-subset(mendsc.eset, rownames(mensc.eset) %in% as.character(probesetids$man_fsetid)) #mensc.est is normalized using expresso function > write.csv(dataset.1.mensc, "dataset.1.mensc.csv") > dim(dataset.1.mensc) > > > Shahenda > > [[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 -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099
ADD COMMENT

Login before adding your answer.

Traffic: 910 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6