R/getSumreplicateschild.R

In yufree/xMSannotator: xMSannotator: R package for network based annotation of metabolomics data.

getSumreplicateschild <- function(curdata, alignment.tool, 
    numreplicates, rep.max.missing.thresh, method = "mean", 
    missing.val) {
    # curdata<-t(curdata)
    # write.table(curdata,file='test.txt',sep='\t',row.names=FALSE)
    numfeats = dim(curdata)[1]
    numsamp = dim(curdata)[2]
    # if(FALSE){
    resvec_1 <- lapply(1:numfeats, function(r) {
        newrow = {
        }
        finalmat = {
        }
        # for(samp in seq(1,(numsamp),numreplicates))
        {
            # i=samp j=i+numreplicates-1
            
            curdata_int = curdata[r, ]
            
            if (is.na(missing.val) == FALSE) {
                check_zeros = which(curdata_int == missing.val)
            } else {
                check_zeros = which(is.na(curdata_int) == 
                  TRUE)
            }
            na_thresh = round(rep.max.missing.thresh * numreplicates)
            
            
            if (length(check_zeros) > na_thresh) {
                meanval <- missing.val
            } else {
                # temporarily replace the missing intensities, set to 0
                # in apLCMS, with mean intensity value of the
                # corresponding replicates (with non-zero values)
                # curdata_int[check_zeros]=mean(t(curdata_int[-c(check_zeros)]))
                if (length(check_zeros) > 0) {
                  if (method == "mean") {
                    meanval <- mean(t(curdata_int[-check_zeros]), 
                      na.rm = TRUE)
                  } else {
                    meanval <- median(t(curdata_int[-check_zeros]), 
                      na.rm = TRUE)
                  }
                } else {
                  if (method == "mean") {
                    meanval <- mean(t(curdata_int), na.rm = TRUE)
                  } else {
                    meanval <- median(t(curdata_int), na.rm = TRUE)
                  }
                }
                
            }
            newrow <- cbind(newrow, meanval)
        }
        
        
        finalmat <- rbind(finalmat, newrow)
        return(finalmat)
    })
    
    # colnames(final_set)<-colnames_data
    # rownames(final_set)=NULL
    return(resvec_1)
    
    
    
}