R/getSumreplicates.R

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

getSumreplicates <- function(curdata, alignment.tool, numreplicates, 
    numcluster, rep.max.missing.thresh, summary.method = "mean", 
    summary.na.replacement = "zeros", missing.val = 0) {
    mean_replicate_difference <- {
    }
    sd_range_duplicate_pairs <- {
    }
    # print(alignment.tool)
    if (alignment.tool == "apLCMS") {
        col_end = 2
    } else {
        if (alignment.tool == "XCMS") {
            col_end = 2
        } else {
            stop(paste("Invalid value for alignment.tool. Please use either \"apLCMS\" or \"XCMS\"", 
                sep = ""))
        }
    }
    
    curdata_mz_rt_info = curdata[, c(1:col_end)]
    curdata = curdata[, -c(1:col_end)]
    
    
    
    cl <- makeCluster(numcluster)
    numfeats = dim(curdata)[1]
    numsamp = dim(curdata)[2]
    
    clusterEvalQ(cl, "getSumreplicateschild")
    sub_samp_list <- list()
    
    sampcount = 1
    for (samp in seq(1, (numsamp), numreplicates)) {
        i = samp
        j = i + numreplicates - 1
        if (dim(curdata[, c(i:j)])[1] > 0) {
            sub_samp_list[[sampcount]] = curdata[, c(i:j)]
        }
        sampcount = sampcount + 1
    }
    
    avg.res <- parSapply(cl, sub_samp_list, getSumreplicateschild, 
        alignment.tool = alignment.tool, numreplicates = numreplicates, 
        rep.max.missing.thresh = rep.max.missing.thresh, 
        method = summary.method, missing.val = missing.val)
    # avg.res<-getAvgreplicateschild(sub_samp_list[[1]],alignment.tool,numreplicates)
    # print('done')
    
    
    stopCluster(cl)
    
    
    
    final_set <- as.data.frame(avg.res)
    colnames_data <- colnames(curdata)
    colnames_data <- colnames_data[seq(1, (numsamp), numreplicates)]
    colnames(final_set) <- colnames_data
    rownames(final_set) = NULL
    # final_set<-cbind(curdata_mz_rt_info,final_set)
    
    final_set <- apply(final_set, 2, as.numeric)
    
    
    if (summary.na.replacement == "zeros") {
        
        final_set <- replace(final_set, which(is.na(final_set) == 
            TRUE), 0)
    } else {
        if (summary.na.replacement == "halfsamplemin") {
            
            
            final_set <- apply(final_set, 2, function(x) {
                naind <- which(is.na(x) == TRUE)
                if (length(naind) > 0) {
                  x[naind] <- min(x, na.rm = TRUE)/2
                }
                return(x)
            })
        } else {
            
            if (summary.na.replacement == "halfdatamin") {
                
                
                min_val <- min(final_set, na.rm = TRUE) * 
                  0.5
                final_set <- replace(final_set, which(is.na(final_set) == 
                  TRUE), min_val)
                
                # data_m<-apply(data_m,1,function(x){naind<-which(is.na(x)==TRUE);
                # if(length(naind)>0){x[naind]<-min(x,na.rm=TRUE)/2};
                # return(x)})
            } else {
                if (summary.na.replacement == "halffeaturemin") {
                  
                  
                  final_set <- apply(final_set, 1, function(x) {
                    naind <- which(is.na(x) == TRUE)
                    if (length(naind) > 0) {
                      x[naind] <- min(x, na.rm = TRUE)/2
                    }
                    return(x)
                  })
                  final_set <- t(final_set)
                }
            }
        }
        
        
    }
    
    return(final_set)
}