[R] Help on averaging sets of rows defined by row name

[Booman, M]

Dear all,

This is my problem: I have a table of gene expression data, where 1st column is gene name, and 2nd -39th columns each are exression data for 38 samples. There are multiple measurements per sample for each gene, so there are multiple rows for each gene name. I want to average these measurements so i end up with one value per sample for each gene name. The output data frame (table.averaged) is further used in other R script. The code I use now (see below) takes 20 secs for each loop, so it takes 45 minutes to average my files of 13500 unique genes. Can anyone help me do this faster?

Cheers, marije

Code I use: 


table.imputed[,1] <- as.character(table.imputed[,1])    #table.imputed is data.frame,1st column = gene name (class factor), rest of columns = expression data (class numeric)

genesunique <- unique(table.imputed[,1])                   #To make list of unique genes in the set

table.averaged <- NULL
  for (j in 1:length(genesunique)) {
     if (j%%100 == 0){                                                   #To report progress
       cat(j, "genes finished", sep=" ", fill=TRUE)
       }
     table.averaged<-rbind(table.averaged,givemean(genesunique[j], table.imputed))   #collects all rows of average values and binds them back into one data frame
  }

givemean <- function (gene, table.imputed) {
   thisgene<-table.imputed[table.imputed[,1]==gene,]                                       #make a subset containing only the rows for one gene name
   data.frame(gene,t(sapply(thisgene[,2:ncol(thisgene)],mean, na.rm=TRUE)))     #calculates average for each sample (column) and outputs one row of average values and the gene name
}


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