R/check.mz.in.replicates.R

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

#' check.mz.in.replicates
#' 
#' Function to find metabolic characteristics of individuals.
#' 
#' This utility allows identification of rare features that are present in only
#' some biological samples, but are present in majority of the analytical
#' replicates of individual samples as a result of unique environmental
#' exposure.  The min.samps and min.reps are user defined values for defining
#' the minimum number of samples and minimum proportion of replicates in which
#' a feature should be detected.
#' 
#' @param dataA Sample intensity matrix from apLCMS or XCMS.  This should only
#' include columns corresponding to samples. All other information such as m/z,
#' retention time, etc. should be deleted.
#' @param min.samps min.samps: minimum number of samples for which a feature
#' signal should be detected in at least min.reps replicates~~
#' @param min.reps minimum proportion of replicates in which a signal is
#' present (eg: 0.5 or 1)
#' @param num_replicates number of replicats for each sample (eg: 2)
#' @return Filtered matrix of features with sample intensities
#' @author Karan Uppal <kuppal2@@emory.edu>
#' @keywords ~replicates
check.mz.in.replicates <- function(dataA, min.samps = 2, 
    min.reps = 2, num_replicates = 3) {
    mean_replicate_difference <- {
    }
    sd_range_duplicate_pairs <- {
    }
    
    curdata <- dataA
    rm(dataA)
    
    # curdata<-curdata[1:10,]
    numfeats = dim(curdata)[1]
    numsamp = dim(curdata)[2]
    rnames <- colnames(curdata)
    rnames <- gsub(".cdf", "", rnames, ignore.case = TRUE)
    quantcolnames = c("min", "first_quartile", "median", 
        "mean", "third_quartile", "max")
    
    
    
    newrow = {
    }
    finalmat = {
    }
    
    
    cl <- makeSOCKcluster(10)
    
    
    
    clusterExport(cl, "check.mz.in.replicates.child")
    
    cv.res <- parApply(cl, curdata, 1, check.mz.in.replicates.child, 
        min.samps = min.samps, min.reps = min.reps, 
        num_replicates = num_replicates)
    print("done")
    dim(cv.res) = dim(matrix(nrow = 1, ncol = numfeats))
    print("done")
    stopCluster(cl)
    # final_set<-as.data.frame(cv.res)
    
    # rownames(final_set)=NULL
    # final_set<-apply(final_set,2,as.numeric)
    # final_set<-as.data.frame(t(final_set))
    # colnames(final_set)<-quantcolnames
    final_set <- curdata[which(cv.res == 1), ]
    # final_set<-cbind(curdata_mz_rt_info,final_set)
    return(final_set)
}