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/*
* Singleerrorrate.cpp
*
* Created on: Jan 23, 2015
* Author: Quentin Marcou
*
* This source code is distributed as part of the IGoR software.
* IGoR (Inference and Generation of Repertoires) is a versatile software to analyze and model immune receptors
* generation, selection, mutation and all other processes.
* Copyright (C) 2017 Quentin Marcou
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <https://www.gnu.org/licenses/>.
*
*/
#include "Singleerrorrate.h"
using namespace std;
Single_error_rate::Single_error_rate(): Single_error_rate(0.0) {}
Single_error_rate::Single_error_rate(double error_rate): Error_rate() , model_rate(error_rate) , normalized_counter(0) , seq_weighted_er(0) {
build_upper_bound_matrix(1,1);
}
Single_error_rate::~Single_error_rate() {
// TODO Auto-generated destructor stub
}
Single_error_rate Single_error_rate::operator +(Single_error_rate err_r){
Single_error_rate temp = *this;
return temp+=err_r;
}
Single_error_rate& Single_error_rate::operator +=(Single_error_rate err_r){
this->normalized_counter+= err_r.normalized_counter;
this->number_seq+=err_r.number_seq;
this->model_log_likelihood+=err_r.model_log_likelihood;
return *this;
}
shared_ptr<Error_rate> Single_error_rate::copy()const{
shared_ptr<Single_error_rate> copy_err_r = shared_ptr<Single_error_rate>(new Single_error_rate(this->model_rate));
copy_err_r->updated = this->updated;
return copy_err_r;
}
Error_rate* Single_error_rate::add_checked(Error_rate* err_r){
return &( this->operator +=( *( dynamic_cast< Single_error_rate*> (err_r) ) ) );
}
const double& Single_error_rate::get_err_rate_upper_bound(size_t n_errors , size_t n_error_free) {
if( n_errors>this->max_err || n_error_free>this->max_noerr){
//Need to increase the matrix size (anyway the matrix is at very most read_len^2
this->build_upper_bound_matrix(max(this->max_err,n_errors + 10) , max(this->max_noerr , n_error_free+10));
}
return this->upper_bound_proba_mat(n_errors,n_error_free);
//return this->model_rate/3;
//TODO remove factor 3
}
void Single_error_rate::build_upper_bound_matrix(size_t m , size_t n){
Matrix<double> new_bound_mat (m,n);
for(size_t i=0 ; i!=new_bound_mat.get_n_rows() ; ++i){
for(size_t j=0 ; j!=new_bound_mat.get_n_cols() ; ++j){
if(i<this->max_err and j<this->max_noerr){
new_bound_mat(i,j) = this->upper_bound_proba_mat(i,j);
}
else{
new_bound_mat(i,j) = pow(this->model_rate/3,i)*pow(1-this->model_rate,j);
}
//new_bound_mat(i,j) = pow(this->model_rate/3,i)*pow(1-this->model_rate,j);
}
}
this->upper_bound_proba_mat = new_bound_mat;
this->max_err = new_bound_mat.get_n_rows()-1;
this->max_noerr = new_bound_mat.get_n_cols()-1;
}
double Single_error_rate::compare_sequences_error_prob (double scenario_probability , const string& original_sequence , Seq_type_str_p_map& constructed_sequences , const Seq_offsets_map& seq_offsets , const unordered_map<tuple<Event_type,Gene_class,Seq_side>, shared_ptr<Rec_Event>>& events_map , Mismatch_vectors_map& mismatches_lists , double& seq_max_prob_scenario , double& proba_threshold_factor){
//TODO extract sequence comparision from here, implement it in Errorrate class??
number_errors=0;
//cout<<constructed_sequences.at(V_gene_seq);
genomic_nucl=0;
Int_Str& v_gene_seq = (*constructed_sequences[V_gene_seq]);
Int_Str& d_gene_seq = (*constructed_sequences[D_gene_seq]);
Int_Str& j_gene_seq = (*constructed_sequences[J_gene_seq]);
vector<int>& v_mismatch_list = *mismatches_lists[V_gene_seq];
if(mismatches_lists.exist(D_gene_seq)){
vector<int>& d_mismatch_list = *mismatches_lists[D_gene_seq];
number_errors+=d_mismatch_list.size();
genomic_nucl+=d_gene_seq.size();
}
vector<int>& j_mismatch_list = *mismatches_lists[J_gene_seq];
genomic_nucl+=v_gene_seq.size();
//genomic_nucl+=d_gene_seq.size();
genomic_nucl+=j_gene_seq.size();
number_errors+=v_mismatch_list.size();
//number_errors+=d_mismatch_list.size();
number_errors+=j_mismatch_list.size();
// Here a long double is required in case a lot of errors occur and/or the model rate is low, the probability will be truncated to 0 if it gets below ± 2.225,073,858,507,201,4 · 10-308 with double precision
scenario_new_proba = scenario_probability*pow(model_rate/3,number_errors)*pow(1-model_rate,genomic_nucl-number_errors);
if(scenario_new_proba >= seq_max_prob_scenario*proba_threshold_factor) {
//if genomic nucl != 0 ?
this->seq_mean_error_number += number_errors*scenario_new_proba;
temp2 = (double(number_errors)/double(genomic_nucl));
temp = scenario_new_proba*temp2;
if(viterbi_run){
this->seq_weighted_er = temp;
this->seq_likelihood = scenario_new_proba;
this->seq_probability = scenario_probability;
}
else{
this->seq_weighted_er += temp;
this->seq_likelihood += scenario_new_proba;
this->seq_probability += scenario_probability;
}
++debug_number_scenarios;
return scenario_new_proba;
}
else{
return 0;
}
}
queue<int> Single_error_rate::generate_errors(string& generated_seq , mt19937_64& generator) const{
uniform_real_distribution<double> distribution(0.0,1.0);
double rand_err ;// distribution(generator);
double rand_trans;
size_t index = 0;
queue<int> errors_indices;
for(string::iterator iter = generated_seq.begin() ; iter != generated_seq.end() ; ++iter){
rand_err = distribution(generator);
if(rand_err<=this->model_rate){
//Introduce an error
rand_trans = distribution(generator);
errors_indices.push(index);
if((*iter) == 'A'){
if(rand_trans<= 1.0/3.0){
(*iter) = 'C';
}
else if (rand_trans >= 2.0/3.0){
(*iter) = 'G';
}
else{
(*iter) = 'T';
}
}
else if((*iter) == 'C'){
if(rand_trans<= 1.0/3.0){
(*iter) = 'A';
}
else if (rand_trans >= 2.0/3.0){
(*iter) = 'G';
}
else{
(*iter) = 'T';
}
}
else if((*iter) == 'G'){
if(rand_trans<= 1.0/3.0){
(*iter) = 'A';
}
else if (rand_trans >= 2.0/3.0){
(*iter) = 'C';
}
else{
(*iter) = 'T';
}
}
else if ((*iter == 'T')){
if(rand_trans<= 1.0/3.0){
(*iter) = 'A';
}
else if (rand_trans >= 2.0/3.0){
(*iter) = 'C';
}
else{
(*iter) = 'G';
}
}
else{
throw runtime_error("unknown nucleotide in Single_error_rate::generate_errors()");
}
}
else{
//Do nothing
}
++index;
}
return errors_indices;
}
int Single_error_rate::subseq_compare_err_num(const string& original_sequence , const string& constructed_sequence){
int number_errors=0;
for (size_t nucl_ind = 0 ; nucl_ind != original_sequence.size() ; ++nucl_ind){
//Only take into account genomic nucl
//might have to refine this if the type of the inserted nucleotide is inferred (use constructed sequences map)
if(original_sequence.at(nucl_ind) != constructed_sequence.at(nucl_ind)){
number_errors+=1;
}
}
return number_errors;
}
void Single_error_rate::update(){
if(this->is_updated()){
model_rate = normalized_counter / number_seq;
normalized_counter = 0;
number_seq = 0;
}
}
/*
* This method ensure correct normalization of the error rate
* The error rate inferred for each scenario is weighted by the probability of the scenario.
* The sum of these is then normalized by the likelihood of the sequence
*/
void Single_error_rate::add_to_norm_counter(){
if(seq_likelihood != 0){ //TODO check that the first version was not more correct
normalized_counter += seq_weighted_er/seq_likelihood;
model_log_likelihood+=log10(seq_likelihood);
number_seq += 1;
}
/*if(seq_weighted_er != 0){ //Why seq weighted error instead of seq likelihood?
normalized_counter += seq_weighted_er/seq_likelihood;
model_log_likelihood+=log10(seq_likelihood);
}*/
seq_weighted_er = 0;
seq_likelihood = 0;
seq_probability = 0;
debug_number_scenarios=0;
seq_mean_error_number=0;
}
/*
* This method cleans the sequence specific counters
* This method is used in case the sequence has a mean number of errors greater than the threshold
* In this case the sequence will not contribute to the error rate
*/
void Single_error_rate::clean_seq_counters(){
seq_weighted_er = 0;
seq_likelihood = 0;
seq_probability = 0;
debug_number_scenarios=0;
seq_mean_error_number=0;
}
void Single_error_rate::write2txt(ofstream& outfile){
outfile<<"#SingleErrorRate"<<endl;
outfile<<model_rate<<endl;
}
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