search for: genesymbol

Dear all, Here is my code which am using to combine 5th column from different data sets. Here is the function to do my job genesymbol.append.file <-NULL gene.column <- NULL readGeneSymbol <- function(files,genesymbol.column=5){ for(i in fnames){ temp <- read.table(fnames,header=T,sep="\t",stringsAsFactors=F,quote="\"") gene.column<-cbind(gene.column,temp[,genesymbol.column]) genesymbol.a...

...xt and file2.txt) which i would like to merge, based on certain criteria, i.e. it combines data based on matching geneID and exons. i have used the merge option, but it does not give me the desired outcome. merged.txt shows the result i would like. *File1. txt* ** AffyProbe ProbeType Flag GeneSymbol GeneID Exons Chrom Strand Affytart AffyEnd 1 1007_s_at:1105:483 0 0 DDR1 780 21 6 + 30975403 30975427 2 1007_s_at:1119:177 0 0 DDR1 780 21 6 + 30975549 30975573 3 1007_s_at:1136:469 0 0 DDR1 780 21 6 + 30975766 30975790 4 1007_s_at:192:205 0 0 DDR1 780 21 6 + 30975523 30975547 5 1007_s_at:474:116...

...ames(design) <- c('100:0', '95:5-100:0') fit <- lmFit(selDataMatrix, design) fit <- eBayes(fit) ## Add gene symbols to gene properties if (require(lumiHumanAll.db) & require(annotate)) { geneSymbol <- getSYMBOL(probeList, 'lumiHumanAll.db') geneName <- sapply(lookUp(probeList, 'lumiHumanAll.db', 'GENENAME'), function(x) x[1]) fit$genes <- data.frame(ID= probeList, geneSymbol=geneSymbol, geneName=geneName, stringsAsFactors=FALSE)...

...xt and file2.txt) which i would like to merge, based on certain criteria, i.e. it combines data based on matching geneID and exons. i have used the merge option, but it does not give me the desired outcome. merged.txt shows the result i would like. *File1. txt* ** AffyProbe ProbeType Flag GeneSymbol GeneID Exons Chrom Strand Affytart AffyEnd 1 1007_s_at:1105:483 0 0 DDR1 780 21 6 + 30975403 30975427 2 1007_s_at:1119:177 0 0 DDR1 780 21 6 + 30975549 30975573 3 1007_s_at:1136:469 0 0 DDR1 780 21 6 + 30975766 30975790 4 1007_s_at:192:205 0 0 DDR1 780 21 6 + 30975523 30975547 5 1007_s_at:474:116...

...ames(design) <- c('100:0', '95:5-100:0') fit <- lmFit(selDataMatrix, design) fit <- eBayes(fit) ## Add gene symbols to gene properties if (require(lumiHumanAll.db) & require(annotate)) { geneSymbol <- getSYMBOL(probeList, 'lumiHumanAll.db') geneName <- sapply(lookUp(probeList, 'lumiHumanAll.db', 'GENENAME'), function(x) x[1]) fit$genes <- data.frame(ID= probeList, geneSymbol=geneSymbol, geneName=geneName, stringsAsFactors=FALSE)...

I deal with a huge amount of Biology data stored in different databases. The databases belongig to Bioconductor organization can be accessed through Bioconductor packages. Unluckily some useful data is stored in databases like, for instance, miRDB, miRecords, etc ... which offer just an interactive HTML interface. See for instance http://mirdb.org/cgi-bin/search.cgi,

R-users, I have the following piece of code which I am trying to run on a dataframe (aga2) with about a half million records.  While the code works, it is extremely slow.  I've read some of the help archives indicating that I should allocate space to the p1 and ags1 vectors, which I have done, but this doesn't seem to improve speed much.  Would anyone be able to provide me with advice on