R/getCVreplicates.child.R

In BowenNCSU/xMSanalyzer: xMSanalyzer: R package for data analysis, comparison, and annotation of metabolomics data.

getCVreplicates.child <- function(curdata, numsamp, 
    numreplicates, min.samp.percent, impute.bool, 
    missingvalue) {
    
    newrow = {
    }
    # numsamp=length(curdata)
    goodsamps <- 0
    for (samp in seq(1, numsamp, numreplicates)) {
        i = samp
        j = i + numreplicates - 1
        
        curdata_int = curdata[c(i:j)]
        
        if (is.na(missingvalue) == TRUE) {
            
            check_zeros = which(is.na(curdata_int) == 
                TRUE)
            
        } else {
            check_zeros = which(curdata_int == missingvalue)
            
        }
        # check_zeros=which(curdata_int==0)
        
        na_thresh = round(min.samp.percent * numreplicates)
        
        
        if (length(check_zeros) >= na_thresh) {
            
            cvval <- NA
            
            # newrow<-cbind(newrow,cvval)
            
        } else {
            # temporarily replace the missing intensities, set
            # to 0 in apLCMS, with mean intensity value of the
            # corresponding replicates (with non-zero values)
            if (length(check_zeros) > 0) {
                if (impute.bool == TRUE) {
                  curdata_int[check_zeros] = mean(t(curdata_int[-c(check_zeros)]))
                }
            }
            sdval <- sd(curdata_int)
            meanval <- mean(t(curdata_int))
            cvval <- 100 * (sdval/meanval)
            newrow <- cbind(newrow, cvval)
            
            goodsamps <- goodsamps + 1
        }
        
    }
    if (length(newrow) > 0) {
        na_ind = which(is.na(newrow) == TRUE)
        
        if (length(na_ind) > 0) {
            sumrow = summary(as.vector(newrow[-na_ind]))
        } else {
            sumrow = summary(as.vector(newrow))
        }
    } else {
        sumrow <- {
        }
    }
    if (length(sumrow) < 6) {
        for (i in 1:6) {
            sumrow[i] = 200
        }
    }
    finalmat <- {
    }
    
    finalmat <- rbind(finalmat, c(unlist(sumrow), 
        goodsamps))
    return(finalmat)
    
}