R/lilikoi.MetaTOpathway.r

In lilikoi: Metabolomics Personalized Pathway Analysis Tool

#' A MetaTOpathway Function
#'
#' This function allows you to convert your metabolites id such as names, kegg ids, pubchem ids.
#' into pathways. Metabolites which have not pathways will be excluded from any downstream analysis
#' make sure that you have three database files which are used for exact and fuzzy matching:
#' cmpd_db.rda, syn_nms_db.rda and Sijia_pathway.rda
#' This function was modified version of the name.match function in the below link:
#' https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R
#' @param q.type The type of the metabolites id such as 'name', 'kegg', 'hmdb','pubchem'
#' @param hmdb if TRUE, match metabolites id to the HMDB database.
#' @param pubchem if TRUE, match metabolites id to the PubChem database.
#' @param chebi if TRUE, match metabolites id to the ChEBI database.
#' @param kegg if TRUE, match metabolites id to the KEGG database.
#' @param metlin if TRUE, match metabolites id to the METLIN database.
#' @importFrom utils write.csv
#' @import dplyr
#' @keywords Match
#' @return A table showing the convertion results from metabolites ids to ids in different metabolomics databases and pathway ids and names.
#' @export
#' @examples
#' \donttest{
#' dt <- lilikoi.Loaddata(file=system.file("extdata",
#'   "plasma_breast_cancer.csv", package = "lilikoi"))
#' Metadata <- dt$Metadata
#' dataSet <- dt$dataSet
#' # Metabolite_pathway_table=lilikoi.MetaTOpathway('name')
#' }
#'
lilikoi.MetaTOpathway<- function(q.type, hmdb=TRUE, pubchem=TRUE, chebi=FALSE, kegg=TRUE, metlin=FALSE){

  ## Insert any pre-composed functions


  ## sanity_check
  sanity_check <- function(name.map){

    todo.inx <-which(is.na(name.map$hit.inx));
    if(length(todo.inx)/length(name.map$hit.inx) > 0.5){
      print("Over half of the compound IDs could not be matched to our database. Please make
            sure that correct compound IDs or common compound names are used.");
    }else if (length(todo.inx) > 100){
      print("There are >100 compounds without matches. You can either proceed or if necessary,
            update these compound IDs and upload again.");
    }else{
      #print("Name matching OK, please inspect (and manual correct) the results then proceed.");
      print( paste0( (length(name.map$hit.inx)-length(todo.inx) ), " ", "out of ",length(name.map$hit.inx)
                     , " ","Matched metabolites"," ",
                     round((length(name.map$hit.inx)/(length(name.map$hit.inx)+length(todo.inx))),1)*100," "
                     ,"%")  );
      cat("\n")
      print( paste0( (length(todo.inx) ), " ", "out of ",length(name.map$hit.inx)
                     , " ","UnMatched metabolites"," ",
                     round((length(todo.inx)/(length(name.map$hit.inx)+length(todo.inx))),1)*100," "
                     ,"%")  );

    }
  }

  ## NameMappingExact

  NameMappingExact<-function(){

    qvec <- dataSet$cmpd;


    # variables to record results
    hit.inx = vector(mode='numeric', length=length(qvec)); # record hit index, initial 0
    match.values = vector(mode='character', length=length(qvec)); # the best matched values (hit names), initial ""
    match.state = vector(mode='numeric', length=length(qvec));  # match status - 0, no match; 1, exact match; initial 0

    # first find exact match to the common compound names
    hit.inx <- match(tolower(qvec), tolower(cmpd.db$name));
    match.values <- cmpd.db$name[hit.inx];
    match.state[!is.na(hit.inx)] <- 1;

    syns.list <-  syn.db$syns.list;

    todo.inx <-which(is.na(hit.inx));
    # assign(which(is.na(hit.inx)), todo.inx, envir = .GlobalEnv)
    if(length(todo.inx) > 0){
      for(i in 1:length(syns.list)){
        syns <-  syns.list[[i]];
        hitInx <- match(tolower(qvec[todo.inx]), tolower(syns));

        hitPos <- which(!is.na(hitInx));
        if(length(hitPos)>0){
          # record matched ones
          orig.inx<-todo.inx[hitPos];
          hit.inx[orig.inx] <- i;
          # match.values[orig.inx] <- syns[hitInx[hitPos]];  # show matched synnames
          match.values[orig.inx] <- cmpd.db$name[i];    # show common name
          match.state[orig.inx] <- 1;

          # update unmatched list
          todo.inx<-todo.inx[is.na(hitInx)];
          # assign(todo.inx[is.na(hitInx)], todo.inx, envir = .GlobalEnv)

          #name.map$hit.inx <<- hit.inx;
          #name.map$hit.values <<- match.values;
          #name.map$match.state <<- match.state;
        }
        if(length(todo.inx) == 0) break;
      }
    }

    # empty memory
    gc();

    name.map$hit.inx <- hit.inx;
    name.map$hit.values <- match.values;
    name.map$match.state <- match.state;


    # GetMappingResultTable<-function(){

      qvec <- dataSet$cmpd;
      if(is.null(qvec)){
        return();
      }
      # style for highlighted background for unmatched names
      pre.style<-NULL;
      post.style<-NULL;

      # style for no matches
      if(dataSet$q.type == "name"){
        no.prestyle<-"<strong style=\"background-color:yellow; font-size=125%; color=\"black\">";
        no.poststyle<-"</strong>";
      }else{
        no.prestyle<-"<strong style=\"background-color:red; font-size=125%; color=\"black\">";
        no.poststyle<-"</strong>";
      }

      hit.inx<-name.map$hit.inx;
      hit.values<-name.map$hit.values;
      match.state<-name.map$match.state;

      # contruct the result table with cells wrapped in html tags
      # the unmatched will be highlighted in different background
      html.res<-matrix("", nrow=length(qvec), ncol=8);
      csv.res<-matrix("", nrow=length(qvec), ncol=8);
      colnames(csv.res)<-c("Query", "Match", "HMDB", "PubChem", "ChEBI", "KEGG", "METLIN", "Comment");

      for (i in 1:length(qvec)){
        if(match.state[i]==1){
          pre.style<-"";
          post.style="";
        }else{ # no matches
          pre.style<-no.prestyle;
          post.style<-no.poststyle;
        }
        hit <-cmpd.db[hit.inx[i], ,drop=F];
        html.res[i, ]<-c(paste(pre.style, qvec[i], post.style, sep=""),
                         paste(ifelse(match.state[i]==0, "", hit.values[i]), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$hmdb_id) || hit$hmdb_id=="" || hit$hmdb_id=="NA","-", paste("<a href=http://www.hmdb.ca/metabolites/", hit$hmdb_id, " target='_blank'>",hit$hmdb_id,"</a>", sep="")),  sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$pubchem_id) || hit$pubchem_id=="" || hit$pubchem_id=="NA", "-", paste("<a href=http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=", hit$pubchem_id," target='_blank'>", hit$pubchem_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$chebi_id) || hit$chebi_id==""|| hit$chebi_id=="NA","-", paste("<a href=http://www.ebi.ac.uk/chebi/searchId.do?chebiId=", hit$chebi_id, " target='_blank'>",hit$chebi_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$kegg_id) || hit$kegg_id==""|| hit$kegg_id=="NA","-",paste("<a href=http://www.genome.jp/dbget-bin/www_bget?", hit$kegg_id, " target='_blank'>", hit$kegg_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$metlin_id) || hit$metlin_id==""|| hit$metlin_id=="NA","-",paste("<a href=http://metlin.scripps.edu/metabo_info.php?molid=", hit$metlin_id," target='_blank'>",hit$metlin_id,"</a>", sep="")), sep=""),
                         ifelse(match.state[i]!=1,"View",""));
        csv.res[i, ]<-c(qvec[i],
                        ifelse(match.state[i]==0, "NA", hit.values[i]),
                        ifelse(match.state[i]==0, "NA", hit$hmdb_id),
                        ifelse(match.state[i]==0, "NA", hit$pubchem_id),
                        ifelse(match.state[i]==0, "NA", hit$chebi_id),
                        ifelse(match.state[i]==0, "NA", hit$kegg_id),
                        ifelse(match.state[i]==0, "NA", hit$metlin_id),
                        match.state[i]);
      }
      # return only columns user selected

      # add query and match columns at the the beginning, and 'Detail' at the end
      return.cols <- c(TRUE, TRUE, return.cols, TRUE);
      html.res <- html.res[,return.cols, drop=F];
      csv.res <- csv.res[,return.cols, drop=F];

      # store the value for report
      dataSet$map.table <- csv.res;
      # assign(csv.res, dataSet$map.table, envir = .GlobalEnv)
      write.csv(csv.res, file="name_map.csv", row.names=F);
      #return(as.vector(html.res));
    # }

    # GetMappingResultTable();

    for (xx in 1:length(todo.inx)){

      ## PerformApproxMatch

        inx <- todo.inx[xx]
        q <- dataSet$cmpd[inx];
        # only for none lipids
        #nonLipidInx <- cmpd.db$lipid == 0;

        #     com.nms <- cmpd.db$name[nonLipidInx]
        #     syns.vec <- syn.db$syns.vec[nonLipidInx];
        #     syns.list <- syn.db$syns.list[nonLipidInx];

        com.nms <- cmpd.db$name;
        syns.vec <- syn.db$syns.vec;
        syns.list <- syn.db$syns.list;

        matched.dist <- NULL;
        q.length <- nchar(q);
        s <- c(0);
        for (j in s) {
          new.q <- q;
          if(q.length > 32){ # note: agrep fail for exact match when length over 32 characters
            new.q<-substr(q, 1, 32);
            #new.q <- unlist(lapply(q,function(x){substr(q, 1, 32)})) ;
          }
          matched <- FALSE;
          matched.inx <- agrep(q, syns.vec, ignore.case=TRUE, max.distance=j, useBytes=TRUE);
          #matched.inx <- unlist(sapply(new.q, function(x) {agrep(x, syns.vec,ignore.case =T, max.distance=j, useBytes=T ) } ))
          if(length(matched.inx) > 0) {
            # record all the candidates,
            # don't use cbind, since all will be converted to character mode
            # for data.frame specify "stringsAsFactors" to prevent convert value col into factor
            candidates <- data.frame(index=vector(mode = "numeric", length=length(matched.inx)),
                                     value=vector(mode = "character", length=length(matched.inx)),
                                     score=vector(mode = "numeric", length=length(matched.inx)),
                                     stringsAsFactors = FALSE);

            for(n in 1:length(matched.inx)){
              nm.vec<-syns.list[[matched.inx[n]]];
              # try approximate match, note: in some cases, split into element will break match using whole string
              hit3.inx <- agrep(q, nm.vec,ignore.case=TRUE, max.distance=j, useBytes=TRUE);
              #hit3.inx <- unlist(sapply(q, function(x) {agrep(x, nm.vec,ignore.case =T, max.distance=j, useBytes=T ) } ))
              if(length(hit3.inx)>0){
                hit3.nm <- vector(mode = "character", length=length(hit3.inx));
                hit3.score <- vector(mode = "numeric", length=length(hit3.inx));
                for(k in 1:length(hit3.inx)){
                  idx <- hit3.inx[k];
                  hit3.nm[k] <- nm.vec[idx];
                  hit3.score[k] <- j + abs(nchar(nm.vec[idx])-nchar(q))/(10*nchar(q));
                }

                # now get the best match, the rule is that the first two character should matches
                # first check if first two character are digits or characters, otherwise will cause error
                matches2 <- c();
                if(length(grep("^[1-9a-z]{2}", q, ignore.case=TRUE))>0){
                  matches2 <- grep(paste("^", substr(q, 1, 2), sep=""), hit3.nm);
                }else if (length(grep("^[1-9a-z]", q, ignore.case=TRUE))>0){
                  matches2 <- grep(paste("^", substr(q, 1, 1), sep=""), hit3.nm);
                }

                if(length(matches2)>0){
                  hit3.score[matches2] <- hit3.score[matches2] - 0.05;
                }

                best.inx<-which(hit3.score==min(hit3.score))[1];
                candidates[n,1]<-matched.inx[n];
                #    candidates[n,2]<-hit3.nm[best.inx]; # show matched syn names
                candidates[n,2]<-com.nms[matched.inx[n]] # show common names
                candidates[n,3]<-hit3.score[best.inx];
              }
            }
            rm.inx <- is.na(candidates[,2]) | candidates[,2]=="NA" | candidates[,2]=="";
            candidates<-candidates[!rm.inx, ];
            candidates<-candidates[order(candidates[,3], decreasing=F), , drop=F];
            if(nrow(candidates) > 10){
              candidates<-candidates[1:10,];
            }
            candidates <- candidates;
            #return();
          }
        }

        candidates <- NULL;
      # }


      # PerformApproxMatch(todo.inx[xx])
      if(is.null(candidates)){
        next;
      }else{


        SetCandidate(todo.inx[xx],1)
      }
    }

    PathMapping<-function(qvec){

      met_W_pathway=HMDBlib[match(qvec[,'HMDB'],HMDBlib$Accession),,drop=F]
      dataSet$map.table1 <- data.frame(qvec) %>%
        mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway)
      # assign(data.frame(qvec) %>%
      #          mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway), dataSet$map.table1, envir = .GlobalEnv)
      write.csv(dataSet$map.table1, file="name_map.csv", row.names=F);
      return(dataSet$map.table1)
    }

    x=PathMapping(dataSet$map.table)
    returnList <- list("table"=x, "check"=sanity_check(name.map))
    return(returnList)

  }

  ## IDMapping

  IDMapping<-function(q.type){

    qvec <- dataSet$cmpd;
    # variables to record results
    hit.inx = vector(mode='numeric', length=length(qvec)); # record hit index, initial 0
    match.values = vector(mode='character', length=length(qvec)); # the best matched values (hit names), initial ""
    match.state = vector(mode='numeric', length=length(qvec)); # match status - 0, no match; 1, exact match; 2 approximate match. initial 0

    if(q.type == "hmdb"){
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$hmdb));
    }else if(q.type == "pubchem"){
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$pubchem));
    }else if(q.type == "chebi"){
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$chebi));
    }else if(q.type == "metlin"){
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$metlin));
    }else if(q.type == "kegg"){
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$kegg));
    }else{ # hmdb + kegg
      hit.inx <- match(tolower(qvec), tolower(cmpd.db$hmdb));
      hit.inx2 <- match(tolower(qvec), tolower(cmpd.db$kegg));
      nohmdbInx <- is.na(hit.inx);
      hit.inx[nohmdbInx]<-hit.inx2[nohmdbInx]
    }

    match.values <- cmpd.db$name[hit.inx];
    match.state[!is.na(hit.inx)] <- 1;

    name.map$hit.inx <- hit.inx;
    name.map$hit.values <- match.values;
    name.map$match.state <- match.state;

    ## GetMappingResultTable
    # GetMappingResultTable<-function(){

      qvec <- dataSet$cmpd;
      if(is.null(qvec)){
        return();
      }
      # style for highlighted background for unmatched names
      pre.style<-NULL;
      post.style<-NULL;

      # style for no matches
      if(dataSet$q.type == "name"){
        no.prestyle<-"<strong style=\"background-color:yellow; font-size=125%; color=\"black\">";
        no.poststyle<-"</strong>";
      }else{
        no.prestyle<-"<strong style=\"background-color:red; font-size=125%; color=\"black\">";
        no.poststyle<-"</strong>";
      }

      hit.inx<-name.map$hit.inx;
      hit.values<-name.map$hit.values;
      match.state<-name.map$match.state;

      # contruct the result table with cells wrapped in html tags
      # the unmatched will be highlighted in different background
      html.res<-matrix("", nrow=length(qvec), ncol=8);
      csv.res<-matrix("", nrow=length(qvec), ncol=8);
      colnames(csv.res)<-c("Query", "Match", "HMDB", "PubChem", "ChEBI", "KEGG", "METLIN", "Comment");

      for (i in 1:length(qvec)){
        if(match.state[i]==1){
          pre.style<-"";
          post.style="";
        }else{ # no matches
          pre.style<-no.prestyle;
          post.style<-no.poststyle;
        }
        hit <-cmpd.db[hit.inx[i], ,drop=F];
        html.res[i, ]<-c(paste(pre.style, qvec[i], post.style, sep=""),
                         paste(ifelse(match.state[i]==0, "", hit.values[i]), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$hmdb_id) || hit$hmdb_id=="" || hit$hmdb_id=="NA","-", paste("<a href=http://www.hmdb.ca/metabolites/", hit$hmdb_id, " target='_blank'>",hit$hmdb_id,"</a>", sep="")),  sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$pubchem_id) || hit$pubchem_id=="" || hit$pubchem_id=="NA", "-", paste("<a href=http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=", hit$pubchem_id," target='_blank'>", hit$pubchem_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$chebi_id) || hit$chebi_id==""|| hit$chebi_id=="NA","-", paste("<a href=http://www.ebi.ac.uk/chebi/searchId.do?chebiId=", hit$chebi_id, " target='_blank'>",hit$chebi_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$kegg_id) || hit$kegg_id==""|| hit$kegg_id=="NA","-",paste("<a href=http://www.genome.jp/dbget-bin/www_bget?", hit$kegg_id, " target='_blank'>", hit$kegg_id,"</a>", sep="")), sep=""),
                         paste(ifelse(match.state[i]==0 || is.na(hit$metlin_id) || hit$metlin_id==""|| hit$metlin_id=="NA","-",paste("<a href=http://metlin.scripps.edu/metabo_info.php?molid=", hit$metlin_id," target='_blank'>",hit$metlin_id,"</a>", sep="")), sep=""),
                         ifelse(match.state[i]!=1,"View",""));
        csv.res[i, ]<-c(qvec[i],
                        ifelse(match.state[i]==0, "NA", hit.values[i]),
                        ifelse(match.state[i]==0, "NA", hit$hmdb_id),
                        ifelse(match.state[i]==0, "NA", hit$pubchem_id),
                        ifelse(match.state[i]==0, "NA", hit$chebi_id),
                        ifelse(match.state[i]==0, "NA", hit$kegg_id),
                        ifelse(match.state[i]==0, "NA", hit$metlin_id),
                        match.state[i]);
      }
      # return only columns user selected

      # add query and match columns at the the beginning, and 'Detail' at the end
      return.cols <- c(TRUE, TRUE, return.cols, TRUE);
      html.res <- html.res[,return.cols, drop=F];
      csv.res <- csv.res[,return.cols, drop=F];

      # store the value for report
      dataSet$map.table <- csv.res;
      # assign(csv.res, dataSet$map.table, envir = .GlobalEnv)
      write.csv(csv.res, file="name_map.csv", row.names=F);
      #return(as.vector(html.res));
    # }

    # GetMappingResultTable();

      ## PathMapping
      PathMapping<-function(qvec){

        met_W_pathway=HMDBlib[match(qvec[,'HMDB'],HMDBlib$Accession),,drop=F]
        dataSet$map.table1 <- data.frame(qvec) %>%
          mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway)
        # assign(data.frame(qvec) %>%
        #          mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway), dataSet$map.table1, envir = .GlobalEnv)
        write.csv(dataSet$map.table1, file="name_map.csv", row.names=F);
        return(dataSet$map.table1)
      }


    # PathMapping(dataSet$map.table)

    x=PathMapping(dataSet$map.table)
    returnList <- list("table"=x, "check"=sanity_check(name.map))
    return(returnList)
  }






  ## PerformApproxMatch

  PerformApproxMatch<-function(inx){
    #print(inx)
    q <- dataSet$cmpd[inx];
    # only for none lipids
    #nonLipidInx <- cmpd.db$lipid == 0;

    #     com.nms <- cmpd.db$name[nonLipidInx]
    #     syns.vec <- syn.db$syns.vec[nonLipidInx];
    #     syns.list <- syn.db$syns.list[nonLipidInx];

    com.nms <- cmpd.db$name;
    syns.vec <- syn.db$syns.vec;
    syns.list <- syn.db$syns.list;

    matched.dist <- NULL;
    q.length <- nchar(q);
    s <- c(0);
    for (j in s) {
      new.q <- q;
      if(q.length > 32){ # note: agrep fail for exact match when length over 32 characters
        new.q<-substr(q, 1, 32);
        #new.q <- unlist(lapply(q,function(x){substr(q, 1, 32)})) ;
      }
      matched <- FALSE;
      matched.inx <- agrep(q, syns.vec, ignore.case=TRUE, max.distance=j, useBytes=TRUE);
      #matched.inx <- unlist(sapply(new.q, function(x) {agrep(x, syns.vec,ignore.case =T, max.distance=j, useBytes=T ) } ))
      if(length(matched.inx) > 0) {
        # record all the candidates,
        # don't use cbind, since all will be converted to character mode
        # for data.frame specify "stringsAsFactors" to prevent convert value col into factor
        candidates <- data.frame(index=vector(mode = "numeric", length=length(matched.inx)),
                                 value=vector(mode = "character", length=length(matched.inx)),
                                 score=vector(mode = "numeric", length=length(matched.inx)),
                                 stringsAsFactors = FALSE);

        for(n in 1:length(matched.inx)){
          nm.vec<-syns.list[[matched.inx[n]]];
          # try approximate match, note: in some cases, split into element will break match using whole string
          hit3.inx <- agrep(q, nm.vec,ignore.case=TRUE, max.distance=j, useBytes=TRUE);
          #hit3.inx <- unlist(sapply(q, function(x) {agrep(x, nm.vec,ignore.case =T, max.distance=j, useBytes=T ) } ))
          if(length(hit3.inx)>0){
            hit3.nm <- vector(mode = "character", length=length(hit3.inx));
            hit3.score <- vector(mode = "numeric", length=length(hit3.inx));
            for(k in 1:length(hit3.inx)){
              idx <- hit3.inx[k];
              hit3.nm[k] <- nm.vec[idx];
              hit3.score[k] <- j + abs(nchar(nm.vec[idx])-nchar(q))/(10*nchar(q));
            }

            # now get the best match, the rule is that the first two character should matches
            # first check if first two character are digits or characters, otherwise will cause error
            matches2 <- c();
            if(length(grep("^[1-9a-z]{2}", q, ignore.case=TRUE))>0){
              matches2 <- grep(paste("^", substr(q, 1, 2), sep=""), hit3.nm);
            }else if (length(grep("^[1-9a-z]", q, ignore.case=TRUE))>0){
              matches2 <- grep(paste("^", substr(q, 1, 1), sep=""), hit3.nm);
            }

            if(length(matches2)>0){
              hit3.score[matches2] <- hit3.score[matches2] - 0.05;
            }

            best.inx<-which(hit3.score==min(hit3.score))[1];
            candidates[n,1]<-matched.inx[n];
            #    candidates[n,2]<-hit3.nm[best.inx]; # show matched syn names
            candidates[n,2]<-com.nms[matched.inx[n]] # show common names
            candidates[n,3]<-hit3.score[best.inx];
          }
        }
        rm.inx <- is.na(candidates[,2]) | candidates[,2]=="NA" | candidates[,2]=="";
        candidates<-candidates[!rm.inx, ];
        candidates<-candidates[order(candidates[,3], decreasing=F), , drop=F];
        if(nrow(candidates) > 10){
          candidates<-candidates[1:10,];
        }
        candidates <- candidates;

        # assign(candidates, candidates, envir = .GlobalEnv);
        #return();
      }
    }

    candidates <- NULL;
    # assign(NULL, candidates, envir = .GlobalEnv);

  }


  ## SetCandidate
  SetCandidate<-function(query_inx, can_inx){
    can_mat<- candidates;
    if(can_inx <= nrow(can_mat)){
      name.map$hit.inx[query_inx] <- can_mat[can_inx,1];
      name.map$hit.values[query_inx] <- can_mat[can_inx,2];
      name.map$match.state[query_inx] <- 1;
      # assign(can_mat[can_inx,1], name.map$hit.inx[query_inx], envir = .GlobalEnv)
      # assign(can_mat[can_inx,2], name.map$hit.values[query_inx], envir = .GlobalEnv)
      # assign(1, name.map$match.state[query_inx], envir = .GlobalEnv)

      # re-generate the CSV file
      hit <-cmpd.db[name.map$hit.inx[query_inx], ,drop=F];
      csv.res <- dataSet$map.table;
      if(ncol(csv.res) > 6){ # general utilities
        csv.res[query_inx, ]<-c(csv.res[query_inx, 1],
                                name.map$hit.values[query_inx],
                                hit$hmdb_id,
                                hit$pubchem_id,
                                hit$chebi_id,
                                hit$kegg_id,
                                hit$metlin_id,
                                1);
      }else{
        csv.res[query_inx, ]<-c(csv.res[query_inx, 1],
                                name.map$hit.values[query_inx],
                                hit$hmdb_id,
                                hit$pubchem_id,
                                hit$kegg_id,
                                1);
      }
      write.csv(csv.res, file="name_map.csv", row.names=F);
      dataSet$map.table <- csv.res;
      # assign(csv.res, dataSet$map.table, envir = .GlobalEnv)

    }else{ #no match
      name.map$hit.inx[query_inx] <- 0;
      name.map$hit.values[query_inx] <- "";
      name.map$match.state[query_inx] <- 0;
    }
  }


  # ## PathMapping
  # PathMapping<-function(qvec){
  #
  #   met_W_pathway=HMDBlib[match(qvec[,'HMDB'],HMDBlib$Accession),,drop=F]
  #   dataSet$map.table1 <- data.frame(qvec) %>%
  #     mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway)
  #   # assign(data.frame(qvec) %>%
  #   #          mutate(pathway=met_W_pathway$Pathway_Name) %>% arrange(pathway), dataSet$map.table1, envir = .GlobalEnv)
  #   write.csv(dataSet$map.table1, file="name_map.csv", row.names=F);
  #   return(dataSet$map.table1)
  # }




  ## The MetaTOpathway function starts from here

  # record the filter for 8 major databases
  return.cols <- c(hmdb, pubchem, chebi, kegg, metlin);

  # record all the data
  # if(!exists("name.map")){
  name.map <- list();
  # }

  # distribute job
  dataSet$q.type <- q.type;

  if(q.type == "name"){
    NameMappingExact();
    # sanity_check(name.map)

  }else{
    IDMapping(q.type);
    # sanity_check(name.map)
  }
}