R/find.Overlapping.mzsvparallel.R

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

find.Overlapping.mzsvparallel <- function(dataA, dataB, mz.thresh = 10, 
    time.thresh = NA, alignment.tool = NA, num_nodes = 2) {
    
    data_a <- as.data.frame(dataA)
    data_b <- as.data.frame(dataB)
    # data_a<-unique(data_a)
    rm(dataA)
    rm(dataB)
    
    
    com_mz_num = 1
    unique_mz = {
    }
    ppm_v = {
    }
    rt_v = {
    }
    
    commat = {
    }
    
    col.names.dataA = colnames(data_a)
    col.names.dataB = colnames(data_b)
    
    if (is.na(alignment.tool) == FALSE) {
        if (alignment.tool == "apLCMS") {
            sample.col.start = 5
        } else {
            if (alignment.tool == "XCMS") {
                sample.col.start = 9
                col.names.dataA[1] = "mz"
                col.names.dataA[2] = "time"
                col.names.dataB[1] = "mz"
                col.names.dataB[2] = "time"
                colnames(data_a) = col.names.dataA
                colnames(data_b) = col.names.dataB
            }
            
        }
    } else {
        # stop(paste('Invalid value for alignment.tool. Please
        # use either \'apLCMS\' or \'XCMS\'', sep=''))
        
        col.names.dataA[1] = "mz"
        
        col.names.dataB[1] = "mz"
        if (is.na(time.thresh) == FALSE) {
            col.names.dataA[2] = "time"
            col.names.dataB[2] = "time"
            # print('Using the 1st columns as \'mz\' and 2nd
            # columsn as \'retention time\'')
        } else {
            # print('Using the 1st columns as \'mz\'')
        }
        colnames(data_a) = col.names.dataA
        colnames(data_b) = col.names.dataB
    }
    
    # data_a<-data_a[order(data_a$mz),]
    # data_b<-data_b[order(data_b$mz),]
    data_a <- as.data.frame(data_a)
    data_b <- as.data.frame(data_b)
    colnames(data_a) = col.names.dataA
    colnames(data_b) = col.names.dataB
    # create header for the matrix with common features
    if (is.na(time.thresh) == FALSE) {
        mznames = c("index.A", "mz.data.A", "time.data.A", 
            "index.B", "mz.data.B", "time.data.B", "time.difference")
    } else {
        mznames = c("index.A", "mz.data.A", "index.B", "mz.data.B")
    }
    
    
    
    # if(FALSE)
    {
        # Step 1 Group features by m/zdim(data_a)[1]
        # mz_groups<-lapply(1:dim(data_a)[1],function(j)
        parallel_list_vec <- 1:dim(data_a)[1]
        
        cl <- makeSOCKcluster(num_nodes)
        clusterExport(cl, "ldply")
        
        
        
        # envir=environment() clusterExport(cl,
        # 'overlapmzchild',envir=environment())
        
        
        mz_groups <- {
        }
        mz_groups <- parLapply(cl, parallel_list_vec, overlapmzchild, 
            mz.thresh = mz.thresh, time.thresh = time.thresh, 
            data_a = data_a, data_b = data_b)
        
        stopCluster(cl)
        # Step 2 Sub-group features from Step 1 by Retention time
        # find the features with RT values within the defined
        # range as compared to the query feature
    }
    uniqueinA = {
    }
    uniqueinB = {
    }
    commat = data.frame()
    
    # save(mz_groups,file='mz_groups.Rda')
    
    if (length(mz_groups) > 0) {
        commat <- ldply(mz_groups, rbind)
    } else {
        
        stop("No mz_groups defined.")
    }
    
    
    
    
    if (is.null(dim(commat)) == FALSE) {
        commat = as.data.frame(commat)
        # colnames(commat)<-mznames
    }
    rm(data_a)
    rm(data_b)
    
    # save(commat,file='commat.Rda')
    
    return(commat)
}