R/tsea_utils.R

In xia-lab/MicrobiomeAnalystR: MicrobiomeAnalystR - A comprehensive R package for statistical, visual, and functional analysis of the microbiome.

############################
###########TSEA#############
############################

GetNameMapCol <-function(mbSetObj, colInx){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  return(mbSetObj$analSet$resTable[,colInx]);
}

GetTseaRowNames <- function(mbSetObj){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  return(rownames(mbSetObj$analSet$tseaInfo));
}

GetTseaCol <-function(mbSetObj, colInx){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  return(mbSetObj$analSet$resTable[,colInx]);
}

#'Function to set up data for TSEA
#'@description This function sets up data for TSEA.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
Setup.MapData<-function(mbSetObj, qvec){

  lines <- unlist(strsplit(qvec, "\r|\n|\r\n")[1]);
  if(substring(lines[1],1,1)=="#"){
    lines <- lines[-1];
  }
  mbSetObj <- .get.mbSetObj(mbSetObj);
  mbSetObj$dataSet$species <- lines;
  return(.set.mbSetObj(mbSetObj))
}

#'Getter function
#'@description This function retrieves table from mbSetObj.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
#'@import xtable
GetORATable<-function(mbSetObj){
  mbSetObj <- .get.mbSetObj(mbSetObj);  
  load_xtable();   
  res <- mbSetObj$analSet$ora.mat;
  print(xtable::xtable(res, caption="Result from Over Representation Analysis"),
        tabular.environment = "longtable", caption.placement="top", size="\\scriptsize");
}

#'Perform cross referencing.
#'@description This function performs cross referencing of user's data
#'with the MicrobiomeAnalyst database. Given a list of species names or ids, 
#'it finds matched names or ids from selected internal databases.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export

CrossReferencing <- function(mbSetObj, q.type){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
  
  # record all the data
  name.map <<- list();
  
  # distribute job
  mbSetObj$dataSet$q.type <- q.type;  
  .set.mbSetObj(mbSetObj)
  
  qvec <- mbSetObj$dataSet$species;
  resTable <- SpeciesMappingExact(qvec, q.type);
  mbSetObj$analSet$resTable <- resTable;
  mbSetObj$analSet$mapTable <- cbind(Query=qvec, resTable[,2:ncol(resTable)]);

  # do some sanity check. note name.map is on global env.
  if(length(which(is.na(name.map$hit.inx)))/length(name.map$hit.inx) > 0.75){
    nmcheck.msg <<- c(1, "Over 3/4 of the IDs could not be matched to our database. Please make 
                        sure that correct taxonomy IDs or common taxa names are used.");        
  }else{
    nmcheck.msg <<- c(1, "Name matching OK, please inspect (and manual correct) the results then proceed.");   
  }  
  return(.set.mbSetObj(mbSetObj))
}

# Utility function
# Mapping from different metabolite IDs
# For compound names to other id, can do exact or approximate match
# For other IDs, except HMDB ID, all other may return multiple /non-unique hits
# multiple hits or non-unique hits will all users to manually select
SpeciesMappingExact<-function(qvec, q.type){
       
  # local variable to save memory
  species.db <- .read.microbiomeanalyst.lib.rds("microbe_db_new.rds", "tsea")
       
  # 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 
       
  if(q.type == "gold"){
    hit.inx <- match(tolower(qvec), tolower(species.db$GOLD_ID));
    match.values <- species.db$GOLD_ID[hit.inx];
    match.state[!is.na(hit.inx)] <- 1;
  }else if(q.type %in% c("mixed","species","strain")){
    hit.inx <- match(tolower(qvec), tolower(species.db$taxa));
    match.values <- species.db$taxa[hit.inx];
    match.state[!is.na(hit.inx)] <- 1;
  }else if(q.type == "ncbitax"){
    hit.inx <- match(tolower(qvec), tolower(species.db$NCBITAX));
    match.values <- species.db$NCBITAX[hit.inx];
    match.state[!is.na(hit.inx)] <- 1;
  }else{
    print(paste("Unknown species ID type:", q.type));
  }
      
  # empty memory
  name.map$hit.inx <- hit.inx;
  name.map$hit.values <- match.values;
  name.map$match.state <- match.state;
  name.map <<- name.map;

  # style for highlighted background for unmatched names
  pre.style <- NULL;
  post.style <- NULL;

  # style for no matches
  no.prestyle <- "<strong style=\"background-color:yellow; 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;

  # construct the result table with cells wrapped in html tags
  # the unmatched will be highlighted in different background
  html.res <- matrix("", nrow=length(qvec), ncol=6);
  colnames(html.res) <- c("Query", "Match", "Species", "Genus", "NCBI_Taxonomy_ID", "GOLDSTAMP_ID");

  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 <- species.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$species) ||is.null(hit$species) || hit$species=="" || hit$species=="NA","-",hit$species),sep=""),
                            paste(ifelse(match.state[i]==0 || is.na(hit$genus) ||is.null(hit$genus) || hit$genus=="" || hit$genus=="NA","-",hit$genus),  sep=""), 
                            paste(ifelse(match.state[i]==0 || is.na(hit$NCBITAX) ||is.null(hit$NCBITAX) || hit$NCBITAX=="" || hit$NCBITAX=="NA","-", paste("<a href=https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=", hit$NCBITAX," target='_blank'>", hit$NCBITAX,"</a>", sep="")),  sep=""),
                            paste(ifelse(match.state[i]==0 || is.na(hit$GOLDMAPID) ||is.null(hit$GOLDMAPID) || hit$GOLDMAPID=="" || hit$GOLDMAPID=="NA", "-", paste("<a href=https://gold.jgi.doe.gov/project?id=", hit$GOLDMAPID," target='_blank'>", hit$GOLDMAPID,"</a>", sep="")), sep=""))
  }

  return(data.frame(html.res,check.names=FALSE));
}

#'Calculate enrichment score.
#'@description This function calculates the enrichment score for TSEA.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
CalculateHyperScore <- function(mbSetObj){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);

  nm.map <- GetFinalNameMap(mbSetObj);
  valid.inx <- !(is.na(nm.map$Strain)| duplicated(nm.map$Strain));
  ora.vec <- nm.map$Strain[valid.inx];

  q.size <- length(ora.vec);
  if(q.size==0) {
    print("Query taxa is missing!");
    return(0);
  }

  if(all(is.na(ora.vec))) {
    print("Query taxa are all NA!");
    return(0);
  }
    
  # total uniq cmpds in the current mset lib
  uniq.count <- length(unique(unlist(current.mset, use.names = FALSE)));
  set.size<-length(current.mset);
    
  if(set.size ==1){
    AddErrMsg("Cannot perform enrichment analysis on a single metabolite set!");
    return(0);
  }

  hits <- lapply(current.mset, function(x){x[x %in% ora.vec]});
  hit.num <- unlist(lapply(hits, function(x) length(x)), use.names = FALSE);
    
  if(sum(hit.num>0)==0){

    AddErrMsg("No matches were found in the selected taxon set library!");

    if(mbSetObj$dataSet$tset.type=="host_int_species"|mbSetObj$dataSet$tset.type=="env_species"|mbSetObj$dataSet$tset.type=="host_ext_species"){
      AddErrMsg("Species-level taxa set was selected: verify that your list contains species names!");
    }else if(mbSetObj$dataSet$tset.type=="host_int_strain"|mbSetObj$dataSet$tset.type=="env_strain"|mbSetObj$dataSet$tset.type=="mic_int_strain"){
      AddErrMsg("Strain-level taxa set was selected: verify that your list contains strain names!");
    }else{
      AddErrMsg("Mixed-level taxa set was selected!");
    }
    return(0);
  }

  set.num<-unlist(lapply(current.mset, length), use.names = FALSE);

  # prepare for the result table
  res.mat<-matrix(NA, nrow=set.size, ncol=6);        
  rownames(res.mat)<-names(current.mset);
  colnames(res.mat)<-c("total", "expected", "hits", "Raw p", "Holm p", "FDR");

  res.mat[,1]<-set.num;
  res.mat[,2]<-q.size*(set.num/uniq.count);
  res.mat[,3]<-hit.num;
  res.mat[,4]<-phyper(hit.num-1, set.num, uniq.count-set.num, q.size, lower.tail=F);

  # adjust for multiple testing problems
  res.mat[,5] <- p.adjust(res.mat[,4], "holm");
  res.mat[,6] <- p.adjust(res.mat[,4], "fdr");

  res.mat <- res.mat[hit.num>0,];

  # fix error when only 1 hit (not sig), no longer a matrix
  if(!("matrix" %in% class(res.mat))){
    AddErrMsg("No significant hits found using enrichment analysis!");
    return(0);
  }

  ord.inx<-order(res.mat[,4]);

  # download result
  mbSetObj$analSet$ora.mat = signif(res.mat[ord.inx,],3);
  mbSetObj$analSet$ora.hits = hits;
  fast.write(mbSetObj$analSet$ora.mat, file="tsea_ora_result.csv");

  return(.set.mbSetObj(mbSetObj));
  
}

#'Getter to return final map
#'@description This function returns the final (after user selection) map as a dataframe.
#'Consists of two columns, original name and strain.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
GetFinalNameMap<-function(mbSetObj){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
  
  enrtype <- mbSetObj$dataSet$q.type;
  qvec <- mbSetObj$dataSet$species;
  nm.mat <- matrix(nrow=length(qvec), ncol=2);
  colnames(nm.mat) <- c("query", "Strain");
    
  if(enrtype=="taxa"){
    for (i in 1:length(qvec)){
      nm.mat[i, ]<-c(qvec[i],qvec[i]);
    }
  }else{
    hit.inx <- name.map$hit.inx;
    hit.values <- name.map$hit.values;
    match.state <- name.map$match.state;
    species.db <- .read.microbiomeanalyst.lib.rds("microbe_db_new.rds", "tsea");
        
    for (i in 1:length(qvec)){
      hit <-species.db[hit.inx[i], ,drop=F];
      if(match.state[i]==0){
        kegg.hit <- NA;
      }else{
        kegg.hit <- ifelse(nchar(hit$organism.name)==0, NA, hit$organism.name);
      }
      nm.mat[i, ] <- c(qvec[i], kegg.hit);
    }
  }
  return(as.data.frame(nm.mat,check.names=FALSE));
}

#'Function to prepare data for enrichment network.
#'@description This function prepares data for enrichment network.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
PrepareEnrichNet<-function(mbSetObj){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
    
  #calculate the enrichment fold change
  folds <- mbSetObj$analSet$ora.mat[,3]/mbSetObj$analSet$ora.mat[,2];
  names(folds) <- GetShortNames(rownames(mbSetObj$analSet$ora.mat));
  hits <- mbSetObj$analSet$ora.mat[,3];
  pvals <- mbSetObj$analSet$ora.mat[,4];
  PlotEnrichNet.Overview(hits, pvals);
}

#'Set the microbe set library
#'@description This function sets the microbe
#'set library for TSEA.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
SetTaxonSetLib <- function(mbSetObj, tset.type){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);

  mbSetObj$dataSet$tset.type <- tset.type
  
  if(.on.public.web){
    if(tset.type=="host_int"){
      libPath <- "../../lib/tsea/tsea_host_int.csv";
    }else if(tset.type=="host_ext"){
      libPath <- "../../lib/tsea/tsea_host_ext.csv";
    }else if(tset.type=="env"){
      libPath <- "../../lib/tsea/tsea_environment.csv";
    }else if(tset.type=="mic_int"){
      libPath <- "../../lib/tsea/tsea_microbiome_int.csv";
    }else if(tset.type=="gene"){
      libPath <- "../../lib/tsea/tsea_host_snps_new.csv";
    }else if(tset.type=="host_int_species") {
      libPath <- "../../lib/tsea/tsea_host_int_species.csv";
    }else if(tset.type=="env_species"){
      libPath <- "../../lib/tsea/tsea_environment_species.csv";
    }else if(tset.type=="host_ext_species"){
      libPath <- "../../lib/tsea/tsea_host_ext_species.csv";
    }else if(tset.type=="host_int_strain"){
      libPath <- "../../lib/tsea/tsea_host_int_strain.csv";
    }else if(tset.type=="host_diet"){
      libPath <- "../../lib/tsea/tsea_host_diet_lifestyle.csv";
    }else if(tset.type=="host_drug"){
      libPath <- "../../lib/tsea/tsea_host_medication.csv";
    }else if(tset.type=="mic_met"){
      libPath <- "../../lib/tsea/taxon_metabolite_tsea.csv";
    }else if(tset.type=="host_diet_species"){
      libPath <- "../../lib/tsea/tsea_host_diet_lifestyle_species.csv";
    }else if(tset.type=="host_drug_species"){
      libPath <- "../../lib/tsea/tsea_host_medication_species.csv";
    }
  }else{
    if(tset.type=="host_int"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_int.csv";
    }else if(tset.type=="host_ext"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_ext.csv";
    }else if(tset.type=="env"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_environment.csv";
    }else if(tset.type=="mic_int"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_microbiome_int.csv";
    }else if(tset.type=="gene"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_snps_new.csv";
    }else if(tset.type=="host_int_species") {
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_int_species.csv";
    }else if(tset.type=="env_species"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_environment_species.csv";
    }else if(tset.type=="host_ext_species"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_ext_species.csv";
    }else if(tset.type=="host_int_strain"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_host_int_strain.csv";
    }else if(tset.type=="env_strain"){
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_environment_strain.csv";
    }else{
      libPath <- "https://www.microbiomeanalyst.ca/MicrobiomeAnalyst/resources/lib/tsea/tsea_microbiome_int_strain.csv";
    }
  }
      
  current.msetlib <<- .readDataTable(libPath);
  ms.list <- strsplit(current.msetlib[,2],"; ");
  names(ms.list) <- current.msetlib[,1];
  current.mset <<- ms.list;
  # total uniq cmpds in the mset lib
  uniq.count <<- length(unique(unlist(current.mset, use.names = FALSE)));
  return(.set.mbSetObj(mbSetObj))
}

#'Create network for enrichmnet overview
#'@description This function creates the plot
#'for the enrichent network overview.
#'@param mbSetObj Input the name of the mbSetObj.
#'@author Jeff Xia \email{jeff.xia@mcgill.ca}
#'McGill University, Canada
#'License: GNU GPL (>= 2)
#'@export
#'@import igraph
#'@import reshape
PlotEnrichNet.Overview<-function(hits, pvals){
  
  load_igraph();
  load_reshape();

  # due to space limitation, plot top 50 if more than 50 were given
  title <- "Taxon Set Enrichment Network Overview";
  if(length(hits) > 50){
    hits <- hits[1:50];
    pvals <- pvals[1:50];
    title <- "Enrichment Overview (top 50)";
  }
  
  pvalue <- pvals;
  id <- names(pvalue);
  geneSets <- current.mset;
  n <- length(pvalue);
  w <- matrix(NA, nrow=n, ncol=n);
  colnames(w) <- rownames(w) <- id;

  for (i in 1:n) {
    for (j in i:n) {
      w[i,j] = overlap_ratio(geneSets[id[i]], geneSets[id[j]])
    }
  }

  wd <- melt(w);
  wd <- wd[wd[,1] != wd[,2],];
  wd <- wd[!is.na(wd[,3]),];
  g <- graph.data.frame(wd[,-3], directed=F);
  E(g)$width <- sqrt(wd[,3]*20);
  g <- delete.edges(g, E(g)[wd[,3] < 0.2]);
  idx <- unlist(sapply(V(g)$name, function(x) match(x,id)));
  pvalue <- pvalue[idx]
  cols <- color_scale("red", "#E5C494");
  V(g)$color <- cols[sapply(pvalue, getIdx, min(pvalue), max(pvalue))];

  cnt <- hits + 2;
  names(cnt) <- id;
  cnt2 <- cnt[V(g)$name];
  V(g)$size <- log(cnt2, base=10) * 10; ## cnt2/sum(cnt2) * 100;
  #V(g)$size <- cnt2/sum(cnt2) * 10;
    
  # layout
  pos.xy <- layout.fruchterman.reingold(g);

  # now create the json object
  nodes <- vector(mode="list");
  node.nms <- V(g)$name;
  node.sizes <- V(g)$size;
  node.cols <- V(g)$color;
    
  for(i in 1:length(node.sizes)){
    nodes[[i]] <- list(id = node.nms[i],
                  label=node.nms[i], 
                  size=node.sizes[i], 
                  color=node.cols[i],
                  x = pos.xy[i,1],
                  y = pos.xy[i,2]);
  }
    
  edge.mat <- get.edgelist(g);
  edge.mat <- cbind(id=1:nrow(edge.mat), source=edge.mat[,1], target=edge.mat[,2]);

  # covert to json
  netData <- list(nodes=nodes, edges=edge.mat);
  sink("tsea_network.json");
  cat(RJSONIO::toJSON(netData));
  sink();
}

#################################
########## Utility Fx ###########
#################################

# Getter for ORA matrix
GetORA.rowNames<-function(mbSetObj){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
  
  nms <- rownames(mbSetObj$analSet$ora.mat);
  
  if(is.null(nms)){
    return("NA");
  }
  return(nms);
}

# Getter
GetORA.mat<-function(mbSetObj){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  return(mbSetObj$analSet$ora.mat);
}

# Getter
GetORA.colorBar<-function(mbSetObj){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
  len <- nrow(mbSetObj$analSet$ora.mat);
  
  if(len > 60){
    ht.col <- c(substr(heat.colors(50), 0, 7), rep("#FFFFFF", len-50));
  }else{
    # reduce to hex by remove the last character so HTML understand
    ht.col <- substr(heat.colors(len), 0, 7);
  }

  return (ht.col);
}

# methods to return the selected metset require to java for display
GetMsetNames<-function(){
  return(current.msetlib$name);
}

GetMsetMembers<-function(){
  return(current.msetlib$member);
}

GetMsetReferences<-function(){
  return(current.msetlib$reference);
}

GetShortNames<-function(nm.vec, max.len= 45){
  new.nms <- vector(mode="character", length=length(nm.vec));
  for(i in 1:length(nm.vec)){
    nm <- nm.vec[i];
    if(nchar(nm) <= max.len){
      new.nms[i] <- nm;
    }else{
      wrds <- strsplit(nm, "[[:space:]]+")[[1]];
      new.nm <- "";
      if(length(wrds)>1){
        for(m in 1:length(wrds)){
          wrd <- wrds[m];
          if(nchar(new.nm)+4+nchar(wrd) <= max.len){
            new.nm <- paste(new.nm, wrd);
          }else{
            new.nms[i] <- paste (new.nm, "...", sep="");
            break;
          }
        }
      }else{
        new.nms[i] <- paste (substr(nm, 0, 21), "...", sep="");
      }
    }
  }
  return (new.nms);
}

overlap_ratio <- function(x, y) {
  x <- unlist(x)
  y <- unlist(y)
  length(intersect(x, y))/length(unique(c(x,y)))
}

#' @importFrom grDevices colorRampPalette
color_scale <- function(c1="grey", c2="red") {
  pal <- grDevices::colorRampPalette(c(c1, c2))
  colors <- pal(100)
  return(colors)
}

getIdx <- function(v, MIN, MAX) {
  if ( MIN == MAX ) {
    return(100)
  }
  intervals <- seq(MIN, MAX, length.out=100)
  max(which(intervals <= v))
}

GetCurrentImg <- function(){
  return (current.img);
}

# given a metset inx, return hmtl highlighted metset cmpds and references
GetHTMLMetSet<-function(mbSetObj, msetNm){
  
  mbSetObj <- .get.mbSetObj(mbSetObj);
  
  hits <- mbSetObj$analSet$ora.hits;
  # highlighting with different colors
  mset <- current.mset[[msetNm]];
  red.inx <- which(mset %in% hits[[msetNm]]);
  mset[red.inx] <- paste("<font color=\"red\">", "<b>", mset[red.inx], "</b>", "</font>",sep="");

  grey.inx <- which(!(mset %in% current.mset[[msetNm]]));
  mset[grey.inx] <- paste("<font color=\"grey\">", "<b>", mset[grey.inx], "</b>", "</font>",sep="");

  # get references
  matched.inx <- match(tolower(msetNm), tolower(current.msetlib$name))[1];

  return(cbind(msetNm, paste(mset, collapse="; "), current.msetlib$reference[matched.inx]));
}

GetMsetPval<-function(mbSetObj, msetNm){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  return(mbSetObj$analSet$ora.mat[msetNm, "Raw p"]);
}

GetMsetEvidence <- function(mbSetObj, msetNm){
  mbSetObj <- .get.mbSetObj(mbSetObj);
  matched.inx <- match(tolower(msetNm), tolower(current.msetlib$name))[1];
  #print(current.msetlib$evidence[matched.inx])
  return(current.msetlib$evidence[matched.inx])
}