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//
// kmerTree.cpp
// pdsBayesian
//
// Created by Patrick Schloss on 4/3/12.
// Copyright (c) 2012 University of Michigan. All rights reserved.
//
#include "kmernode.h"
#include "kmertree.h"
/**************************************************************************************************/
KmerTree::KmerTree(string referenceFileName, string taxonomyFileName, int k, int cutoff) : Classify(), confidenceThreshold(cutoff), kmerSize(k){
try {
KmerNode* newNode = new KmerNode("Root", 0, kmerSize);
tree.push_back(newNode); // the tree is stored as a vector of elements of type TaxonomyNode
int power4s[14] = { 1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864 };
numPossibleKmers = power4s[kmerSize];
string refTaxonomy;
readTaxonomy(taxonomyFileName);
ifstream referenceFile;
m->openInputFile(referenceFileName, referenceFile);
bool error = false;
while(!referenceFile.eof()){
if (m->control_pressed) { break; }
Sequence seq(referenceFile); m->gobble(referenceFile);
if (seq.getName() != "") {
map<string, string>::iterator it = taxonomy.find(seq.getName());
if (it != taxonomy.end()) {
refTaxonomy = it->second; // lookup the taxonomy string for the current reference sequence
vector<int> kmerProfile = ripKmerProfile(seq.getUnaligned()); //convert to kmer vector
addTaxonomyToTree(seq.getName(), refTaxonomy, kmerProfile);
}else {
m->mothurOut(seq.getName() + " is in your reference file, but not in your taxonomy file, please correct.\n"); error = true;
}
}
}
referenceFile.close();
if (error) { m->control_pressed = true; }
numTaxa = (int)tree.size();
numLevels = 0;
for(int i=0;i<numTaxa;i++){
int level = tree[i]->getLevel();
if(level > numLevels){ numLevels = level; }
}
numLevels++;
aggregateThetas();
int dbSize = tree[0]->getNumSeqs();
for(int i=0;i<numTaxa;i++){
tree[i]->checkTheta();
tree[i]->setNumUniqueKmers(tree[0]->getNumUniqueKmers());
tree[i]->setTotalSeqs(dbSize);
}
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "KmerTree");
exit(1);
}
}
/**************************************************************************************************/
KmerTree::~KmerTree(){
for(int i=0;i<tree.size();i++){
delete tree[i];
}
}
/**********************************************************************************************************************/
vector<int> KmerTree::ripKmerProfile(string sequence){
try {
// assume all input sequences are unaligned
int power4s[14] = { 1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864 };
int nKmers = (int)sequence.length() - kmerSize + 1;
vector<int> kmerProfile(numPossibleKmers + 1, 0);
for(int i=0;i<nKmers;i++){
if (m->control_pressed) { break; }
int kmer = 0;
for(int j=0;j<kmerSize;j++){
if(toupper(sequence[j+i]) == 'A') { kmer += (0 * power4s[kmerSize-j-1]); }
else if(toupper(sequence[j+i]) == 'C') { kmer += (1 * power4s[kmerSize-j-1]); }
else if(toupper(sequence[j+i]) == 'G') { kmer += (2 * power4s[kmerSize-j-1]); }
else if(toupper(sequence[j+i]) == 'U') { kmer += (3 * power4s[kmerSize-j-1]); }
else if(toupper(sequence[j+i]) == 'T') { kmer += (3 * power4s[kmerSize-j-1]); }
else { kmer = power4s[kmerSize]; j = kmerSize; }
}
kmerProfile[kmer] = 1;
}
return kmerProfile;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "ripKmerProfile");
exit(1);
}
}
/**************************************************************************************************/
int KmerTree::addTaxonomyToTree(string seqName, string taxonomy, vector<int>& sequence){
try {
KmerNode* newNode;
string taxonName = "";
int treePosition = 0; // the root is element 0
int level = 1;
for(int i=0;i<taxonomy.length();i++){ // step through taxonomy string...
if (m->control_pressed) { break; }
if(taxonomy[i] == ';'){ // looking for semicolons...
if (taxonName == "") { m->mothurOut(seqName + " has an error in the taxonomy. This may be due to a ;;"); m->mothurOutEndLine(); m->control_pressed = true; }
int newIndex = tree[treePosition]->getChildIndex(taxonName);// look to see if your current node already
// has a child with the new taxonName
if(newIndex != -1) { treePosition = newIndex; } // if you've seen it before, jump to that
else { // position in the tree
int newChildIndex = (int)tree.size(); // otherwise, we'll have to create one...
tree[treePosition]->makeChild(taxonName, newChildIndex);
newNode = new KmerNode(taxonName, level, kmerSize);
newNode->setParent(treePosition);
tree.push_back(newNode);
treePosition = newChildIndex;
}
// sequence data to that node to update that node's theta - seems slow...
taxonName = ""; // clear out the taxon name that we will build as we look
level++;
} // for a semicolon
else{
taxonName += taxonomy[i]; // keep adding letters until we reach a semicolon
}
}
tree[treePosition]->loadSequence(sequence); // now that we've gotten to the correct node, add the
return 0;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "addTaxonomyToTree");
exit(1);
}
}
/**************************************************************************************************/
int KmerTree::aggregateThetas(){
try {
vector<vector<int> > levelMatrix(numLevels+1);
for(int i=0;i<tree.size();i++){
if (m->control_pressed) { return 0; }
levelMatrix[tree[i]->getLevel()].push_back(i);
}
for(int i=numLevels-1;i>0;i--) {
if (m->control_pressed) { return 0; }
for(int j=0;j<levelMatrix[i].size();j++){
KmerNode* holder = tree[levelMatrix[i][j]];
tree[holder->getParent()]->addThetas(holder->getTheta(), holder->getNumSeqs());
}
}
return 0;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "aggregateThetas");
exit(1);
}
}
/**************************************************************************************************/
int KmerTree::getMinRiskIndexKmer(vector<int>& sequence, vector<int>& taxaIndices, vector<double>& probabilities){
try {
int numProbs = (int)probabilities.size();
vector<double> G(numProbs, 0.2); //a random sequence will, on average, be 20% similar to any other sequence; not sure that this holds up for kmers; whatever.
vector<double> risk(numProbs, 0);
for(int i=1;i<numProbs;i++){ //use if you want the outlier group
if (m->control_pressed) { return 0; }
G[i] = tree[taxaIndices[i]]->getSimToConsensus(sequence);
}
double minRisk = 1e6;
int minRiskIndex = 0;
for(int i=0;i<numProbs;i++){
if (m->control_pressed) { return 0; }
for(int j=0;j<numProbs;j++){
if(i != j){
risk[i] += probabilities[j] * G[j];
}
}
if(risk[i] < minRisk){
minRisk = risk[i];
minRiskIndex = i;
}
}
return minRiskIndex;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "getMinRiskIndexKmer");
exit(1);
}
}
/**************************************************************************************************/
int KmerTree::sanityCheck(vector<vector<int> >& indices, vector<int>& maxIndices){
try {
int finalLevel = (int)indices.size()-1;
for(int position=1;position<indices.size();position++){
if (m->control_pressed) { return 0; }
int predictedParent = tree[indices[position][maxIndices[position]]]->getParent();
int actualParent = indices[position-1][maxIndices[position-1]];
if(predictedParent != actualParent){
finalLevel = position - 1;
return finalLevel;
}
}
return finalLevel;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "sanityCheck");
exit(1);
}
}
/**************************************************************************************************/
string KmerTree::getTaxonomy(Sequence* thisSeq){
try {
string seqName = thisSeq->getName(); string querySequence = thisSeq->getAligned(); string taxonProbabilityString = "";
string unalignedSeq = thisSeq->getUnaligned();
double logPOutlier = (querySequence.length() - kmerSize + 1) * log(1.0/(double)tree[0]->getNumUniqueKmers());
vector<int> queryProfile = ripKmerProfile(unalignedSeq); //convert to kmer vector
vector<vector<double> > pXgivenKj_D_j(numLevels);
vector<vector<int> > indices(numLevels);
for(int i=0;i<numLevels;i++){
if (m->control_pressed) { return taxonProbabilityString; }
pXgivenKj_D_j[i].push_back(logPOutlier);
indices[i].push_back(-1);
}
for(int i=0;i<numTaxa;i++){
if (m->control_pressed) { return taxonProbabilityString; }
pXgivenKj_D_j[tree[i]->getLevel()].push_back(tree[i]->getPxGivenkj_D_j(queryProfile));
indices[tree[i]->getLevel()].push_back(i);
}
vector<double> sumLikelihood(numLevels, 0);
vector<double> bestPosterior(numLevels, 0);
vector<int> maxIndex(numLevels, 0);
int maxPosteriorIndex;
//let's find the best level and taxa within that level
for(int i=0;i<numLevels;i++){ //go across all j's - from the root to genus
if (m->control_pressed) { return taxonProbabilityString; }
int numTaxaInLevel = (int)indices[i].size();
vector<double> posteriors(numTaxaInLevel, 0);
sumLikelihood[i] = getLogExpSum(pXgivenKj_D_j[i], maxPosteriorIndex);
maxPosteriorIndex = 0;
for(int j=0;j<numTaxaInLevel;j++){
posteriors[j] = exp(pXgivenKj_D_j[i][j] - sumLikelihood[i]);
if(posteriors[j] > posteriors[maxPosteriorIndex]){
maxPosteriorIndex = j;
}
}
maxIndex[i] = getMinRiskIndexKmer(queryProfile, indices[i], posteriors);
maxIndex[i] = maxPosteriorIndex;
bestPosterior[i] = posteriors[maxIndex[i]];
}
// vector<double> pX_level(numLevels, 0);
//
// for(int i=0;i<numLevels;i++){
// pX_level[i] = pXgivenKj_D_j[i][maxIndex[i]] - tree[indices[i][maxIndex[i]]]->getNumSeqs();
// }
//
// int max_pLevel_X_index = -1;
// double pX_level_sum = getLogExpSum(pX_level, max_pLevel_X_index);
// double max_pLevel_X = exp(pX_level[max_pLevel_X_index] - pX_level_sum);
//
// vector<double> pLevel_X(numLevels, 0);
// for(int i=0;i<numLevels;i++){
// pLevel_X[i] = exp(pX_level[i] - pX_level_sum);
// }
int saneDepth = sanityCheck(indices, maxIndex);
// stringstream levelProbabilityOutput;
// levelProbabilityOutput.setf(ios::fixed, ios::floatfield);
// levelProbabilityOutput.setf(ios::showpoint);
//taxonProbabilityOutput << seqName << '\t';
// taxonProbabilityOutput << seqName << '(' << max_pLevel_X_index << ';' << max_pLevel_X << ')' << '\t';
// levelProbabilityOutput << seqName << '(' << max_pLevel_X_index << ';' << max_pLevel_X << ')' << '\t';
simpleTax = "";
int savedspot = 1;
taxonProbabilityString = "";
for(int i=1;i<=saneDepth;i++){
if (m->control_pressed) { return taxonProbabilityString; }
int confidenceScore = (int) (bestPosterior[i] * 100);
if (confidenceScore >= confidenceThreshold) {
if(indices[i][maxIndex[i]] != -1){
taxonProbabilityString += tree[indices[i][maxIndex[i]]]->getName() + "(" + toString(confidenceScore) + ");";
simpleTax += tree[indices[i][maxIndex[i]]]->getName() + ";";
// levelProbabilityOutput << tree[indices[i][maxIndex[i]]]->getName() << '(' << setprecision(6) << pLevel_X[i] << ");";
}
else{
taxonProbabilityString += "unclassified(" + toString(confidenceScore) + ");";
// levelProbabilityOutput << "unclassified" << '(' << setprecision(6) << pLevel_X[i] << ");";
simpleTax += "unclassified;";
}
}else { break; }
savedspot = i;
}
for(int i=savedspot+1;i<numLevels;i++){
if (m->control_pressed) { return taxonProbabilityString; }
taxonProbabilityString += "unclassified(0);";
simpleTax += "unclassified;";
}
return taxonProbabilityString;
}
catch(exception& e) {
m->errorOut(e, "KmerTree", "getTaxonomy");
exit(1);
}
}
/**************************************************************************************************/
|
