*/
#include "bayesian.h"
+#include "kmer.hpp"
/**************************************************************************************************/
-Bayesian::Bayesian(string tfile, string tempFile, string method, int kmerSize, int gapOpen, int gapExtend, int match, int misMatch) :
-Classify(tfile, tempFile, method, kmerSize, gapOpen, gapExtend, match, misMatch) {}
+Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff) :
+Classify(tfile, tempFile, method, ksize, 0, 0, 0, 0), kmerSize(ksize), confidenceThreshold(cutoff) {
+ try {
+
+ numKmers = database->getMaxKmer() + 1;
+
+ //initialze probabilities
+ wordGenusProb.resize(numKmers);
+
+ genusNodes = phyloTree->getGenusNodes();
+
+ for (int j = 0; j < wordGenusProb.size(); j++) { wordGenusProb[j].resize(genusNodes.size()); }
+
+ //reset counts because we are on a new word
+ for (int j = 0; j < genusNodes.size(); j++) {
+ TaxNode temp = phyloTree->get(genusNodes[j]);
+ genusTotals.push_back(temp.accessions.size());
+ }
+
+
+ /************calculate the probablity that each word will be in a specific taxonomy*************/
+ ofstream out;
+ string probFileName = tempFile.substr(0,tempFile.find_last_of(".")+1) + char('0'+ kmerSize) + "mer.prob";
+ ifstream probFileTest(probFileName.c_str());
+
+ ofstream out2;
+ string probFileName2 = tempFile.substr(0,tempFile.find_last_of(".")+1) + char('0'+ kmerSize) + "mer.numNonZero";
+ ifstream probFileTest2(probFileName2.c_str());
+
+ int start = time(NULL);
+
+ if(probFileTest && probFileTest2){
+ mothurOut("Reading template probabilities... "); cout.flush();
+ readProbFile(probFileTest, probFileTest2);
+ }else{
+ mothurOut("Calculating template probabilities... "); cout.flush();
+
+ ofstream out;
+ openOutputFile(probFileName, out);
+
+ ofstream out2;
+ openOutputFile(probFileName2, out2);
+
+ //for each word
+ for (int i = 0; i < numKmers; i++) {
+
+ out << i << '\t';
+
+ vector<int> seqsWithWordi = database->getSequencesWithKmer(i);
+
+ map<int, int> count;
+ for (int k = 0; k < genusNodes.size(); k++) { count[genusNodes[k]] = 0; }
+
+ //for each sequence with that word
+ for (int j = 0; j < seqsWithWordi.size(); j++) {
+ int temp = phyloTree->getIndex(names[seqsWithWordi[j]]);
+ count[temp]++; //increment count of seq in this genus who have this word
+ }
+
+ //probabilityInTemplate = (# of seqs with that word in template + 0.05) / (total number of seqs in template + 1);
+ float probabilityInTemplate = (seqsWithWordi.size() + 0.50) / (float) (names.size() + 1);
+
+ int numNotZero = 0;
+ for (int k = 0; k < genusNodes.size(); k++) {
+ //probabilityInThisTaxonomy = (# of seqs with that word in this taxonomy + probabilityInTemplate) / (total number of seqs in this taxonomy + 1);
+ wordGenusProb[i][k] = log((count[genusNodes[k]] + probabilityInTemplate) / (float) (genusTotals[k] + 1));
+ if (count[genusNodes[k]] != 0) { out << k << '\t' << wordGenusProb[i][k] << '\t'; numNotZero++; }
+ }
+ out << endl;
+ out2 << probabilityInTemplate << '\t' << numNotZero << endl;
+ }
+
+ out.close();
+ out2.close();
+ }
+
+
+ mothurOut("DONE."); mothurOutEndLine();
+ mothurOut("It took " + toString(time(NULL) - start) + " seconds get probabilities. "); mothurOutEndLine();
+ }
+ catch(exception& e) {
+ errorOut(e, "Bayesian", "getTaxonomy");
+ exit(1);
+ }
+}
/**************************************************************************************************/
string Bayesian::getTaxonomy(Sequence* seq) {
try {
string tax;
+ Kmer kmer(kmerSize);
+ //get words contained in query
+ //getKmerString returns a string where the index in the string is hte kmer number
+ //and the character at that index can be converted to be the number of times that kmer was seen
+ string queryKmerString = kmer.getKmerString(seq->getUnaligned());
+ vector<int> queryKmers;
+ for (int i = 0; i < queryKmerString.length(); i++) {
+ if (queryKmerString[i] != '!') { //this kmer is in the query
+ queryKmers.push_back(i);
+ }
+ }
+
+ int index = getMostProbableTaxonomy(queryKmers);
+
+ //bootstrap - to set confidenceScore
+ int numToSelect = queryKmers.size() / 8;
+ tax = bootstrapResults(queryKmers, index, numToSelect);
return tax;
}
}
}
/**************************************************************************************************/
+string Bayesian::bootstrapResults(vector<int> kmers, int tax, int numToSelect) {
+ try {
+
+ //taxConfidenceScore.clear(); //clear out previous seqs scores
+
+ vector< map<string, int> > confidenceScores; //you need the added vector level of confusion to account for the level that name is seen since they can be the same
+ //map of classification to confidence for all areas seen
+ //ie. Bacteria;Alphaproteobacteria;Rhizobiales;Azorhizobium_et_rel.;Methylobacterium_et_rel.;Bosea;
+ //ie. Bacteria -> 100, Alphaproteobacteria -> 100, Rhizobiales -> 87, Azorhizobium_et_rel. -> 78, Methylobacterium_et_rel. -> 70, Bosea -> 50
+ confidenceScores.resize(100); //if you have more than 100 levels of classification...
+
+ map<string, int>::iterator itBoot;
+ map<string, int>::iterator itBoot2;
+ map<int, int>::iterator itConvert;
+
+ for (int i = 0; i < 100; i++) {
+ vector<int> temp;
+
+ for (int j = 0; j < numToSelect; j++) {
+ int index = int(rand() % kmers.size());
+
+ //add word to temp
+ temp.push_back(kmers[index]);
+ }
+
+ //get taxonomy
+ int newTax = getMostProbableTaxonomy(temp);
+ TaxNode taxonomy = phyloTree->get(newTax);
+
+ //add to confidence results
+ while (taxonomy.level != 0) { //while you are not at the root
+
+ itBoot2 = confidenceScores[taxonomy.level].find(taxonomy.name); //is this a classification we already have a count on
+
+ if (itBoot2 == confidenceScores[taxonomy.level].end()) { //not already in confidence scores
+ confidenceScores[taxonomy.level][taxonomy.name] = 1;
+ }else{
+ confidenceScores[taxonomy.level][taxonomy.name]++;
+ }
+
+ taxonomy = phyloTree->get(taxonomy.parent);
+ }
+ }
+
+ string confidenceTax = "";
+ simpleTax = "";
+ TaxNode seqTax = phyloTree->get(tax);
+
+ while (seqTax.level != 0) { //while you are not at the root
+
+ itBoot2 = confidenceScores[seqTax.level].find(seqTax.name); //is this a classification we already have a count on
+
+ int confidence = 0;
+ if (itBoot2 != confidenceScores[seqTax.level].end()) { //not already in confidence scores
+ confidence = confidenceScores[seqTax.level][seqTax.name];
+ }
+
+ if (confidence >= confidenceThreshold) {
+ confidenceTax = seqTax.name + "(" + toString(confidence) + ");" + confidenceTax;
+ simpleTax = seqTax.name + ";" + simpleTax;
+ }
+
+ seqTax = phyloTree->get(seqTax.parent);
+ }
+
+ return confidenceTax;
+
+ }
+ catch(exception& e) {
+ errorOut(e, "Bayesian", "bootstrapResults");
+ exit(1);
+ }
+}
+/**************************************************************************************************/
+int Bayesian::getMostProbableTaxonomy(vector<int> queryKmer) {
+ try {
+ int indexofGenus;
+
+ double maxProbability = -1000000.0;
+ //find taxonomy with highest probability that this sequence is from it
+ for (int k = 0; k < genusNodes.size(); k++) {
+
+ //for each taxonomy calc its probability
+ double prob = 1.0;
+ for (int i = 0; i < queryKmer.size(); i++) {
+ prob += wordGenusProb[queryKmer[i]][k];
+ }
+
+ //is this the taxonomy with the greatest probability?
+ if (prob > maxProbability) {
+ indexofGenus = genusNodes[k];
+ maxProbability = prob;
+ }
+ }
+
+ return indexofGenus;
+ }
+ catch(exception& e) {
+ errorOut(e, "Bayesian", "getMostProbableTaxonomy");
+ exit(1);
+ }
+}
+/*************************************************************************************************
+map<string, int> Bayesian::parseTaxMap(string newTax) {
+ try{
+
+ map<string, int> parsed;
+
+ newTax = newTax.substr(0, newTax.length()-1); //get rid of last ';'
+
+ //parse taxonomy
+ string individual;
+ while (newTax.find_first_of(';') != -1) {
+ individual = newTax.substr(0,newTax.find_first_of(';'));
+ newTax = newTax.substr(newTax.find_first_of(';')+1, newTax.length());
+ parsed[individual] = 1;
+ }
+
+ //get last one
+ parsed[newTax] = 1;
+
+ return parsed;
+
+ }
+ catch(exception& e) {
+ errorOut(e, "Bayesian", "parseTax");
+ exit(1);
+ }
+}
+/**************************************************************************************************/
+void Bayesian::readProbFile(ifstream& in, ifstream& inNum) {
+ try{
+
+ int kmer, name, count; count = 0;
+ vector<int> num; num.resize(numKmers);
+ float prob;
+ vector<float> zeroCountProb; zeroCountProb.resize(numKmers);
+
+ while (inNum) {
+ inNum >> zeroCountProb[count] >> num[count];
+ count++;
+ gobble(inNum);
+ }
+ inNum.close();
+
+ while(in) {
+ in >> kmer;
+
+ //set them all to zero value
+ for (int i = 0; i < genusNodes.size(); i++) {
+ wordGenusProb[kmer][i] = log(zeroCountProb[kmer] / (float) (genusTotals[i]+1));
+ }
+
+ //get probs for nonzero values
+ for (int i = 0; i < num[kmer]; i++) {
+ in >> name >> prob;
+ wordGenusProb[kmer][name] = prob;
+ }
+
+ gobble(in);
+ }
+ in.close();
+ }
+ catch(exception& e) {
+ errorOut(e, "Bayesian", "readProbFile");
+ exit(1);
+ }
+}
+/**************************************************************************************************/
+
+
+
+
+
projects / mothur.git / blobdiff