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PredictHaplo_externAlign.cpp
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PredictHaplo_externAlign.cpp
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/*
PredictHaplo-Paired: a program for estimating haplotypes from "next
generation sequencing" reads Copyright (C) 2014 Volker Roth
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 <http://www.gnu.org/licenses/>.
*/
// TODO: where does one actually get this definition from?
#define R_finite(x) std::isfinite(x)
#include "phaplo/Exception.hpp"
#include "phaplo/ParseStatistics.hpp"
#include "phaplo/binary.hpp"
#include "phaplo/is_alignment_match.hpp"
#include "phaplo/is_cigar_code.hpp"
#include "phaplo/median.hpp"
#include "phaplo/statistics_to_messages.hpp"
#include "phaplo/to_vector_of_ints.hpp"
#include "scythestat/distributions.h"
#include "scythestat/ide.h"
#include "scythestat/la.h"
#include "scythestat/lapack.h"
#include "scythestat/matrix.h"
#include "scythestat/optimize.h"
#include "scythestat/rng.h"
#include "scythestat/rng/mersenne.h"
#include "scythestat/smath.h"
#include "scythestat/stat.h"
#include <algorithm>
#include <cmath>
#include <filesystem>
#include <fstream>
#include <getopt.h>
#include <iostream>
#include <optional>
#include <random>
#include <sstream>
#include <unordered_map>
using namespace scythe;
using namespace std;
template <class T> struct index_cmp {
index_cmp(const T arr) : arr(arr) {}
bool operator()(const size_t a, const size_t b) const {
return arr[a] > arr[b];
}
const T arr;
};
bool myfunction(int i, int j) { return (i > j); }
int visualization_level = 1;
int local_window_size;
vector<string> tokenize(const string &str, const string &delimiters) {
vector<string> tokens;
string::size_type lastPos = 0, pos = 0;
int count = 0;
if (str.length() < 1)
return tokens;
// skip delimiters at beginning.
lastPos = str.find_first_not_of(delimiters, 0);
if ((str.substr(0, lastPos - pos).length()) > 0) {
count = str.substr(0, lastPos - pos).length();
for (int i = 0; i < count; i++)
tokens.push_back("");
if (string::npos == lastPos)
tokens.push_back("");
}
// find first \"non-delimiter\".
pos = str.find_first_of(delimiters, lastPos);
while (string::npos != pos || string::npos != lastPos) {
// found a token, add it to the vector.
tokens.push_back(str.substr(lastPos, pos - lastPos));
// skip delimiters. Note the \"not_of\"
lastPos = str.find_first_not_of(delimiters, pos);
if ((string::npos != pos) &&
(str.substr(pos, lastPos - pos).length() > 1)) {
count = str.substr(pos, lastPos - pos).length();
for (int i = 0; i < count - 1; i++)
tokens.push_back("");
}
pos = str.find_first_of(delimiters, lastPos);
}
return tokens;
}
int qual_subtract;
bool include_deletions = true;
int levels;
int parse_sam_line(const vector<string> &tokens, string &used_qual,
vector<int> &seq_b, double &a_score, const char gap_quality,
int &indels, bool &have_quality_scores,
int &al_start) noexcept {
used_qual.clear();
seq_b.clear();
a_score = std::numeric_limits<double>::quiet_NaN();
indels = 0;
have_quality_scores = true;
al_start = atoi(tokens[3].c_str());
// cout <<"tokens.size(): " << tokens.size() << endl;
for (int j = 11; j < tokens.size(); j++) {
vector<string> tokens_as = tokenize(tokens[j].c_str(), ":");
if (tokens_as[0] == "AS") {
a_score = atof(tokens_as[2].c_str());
break;
}
}
vector<int> isCode(tokens[5].size(), 1);
for (int i = 0; i < tokens[5].size(); i++) {
if (!phaplo::is_cigar_code(tokens[5][i]))
isCode[i] = 0;
}
vector<int> sub_length_vec;
vector<char> symbols;
int sub_length = 0;
for (int i = 0; i < tokens[5].size(); i++) {
if (isCode[i] == 1) {
sub_length_vec.push_back(
atoi(tokens[5].substr(i - sub_length, sub_length).c_str()));
symbols.push_back(tokens[5][i]);
sub_length = 0;
}
if (isCode[i] == 0)
sub_length++;
}
string qual_s = tokens[10];
if (qual_s.size() == 1 && qual_s[0] == '*')
have_quality_scores = false;
int c = 0;
for (int i = 0; i < sub_length_vec.size(); i++) {
if (symbols[i] == 'S')
for (int j = 0; j < sub_length_vec[i]; j++) {
c++;
}
if (phaplo::is_alignment_match(symbols[i]))
for (int j = 0; j < sub_length_vec[i]; j++) {
int k = 0;
if (tokens[9][c] == 'A' || tokens[9][c] == 'a')
k = 1;
else if (tokens[9][c] == 'C' || tokens[9][c] == 'c')
k = 2;
else if (tokens[9][c] == 'G' || tokens[9][c] == 'g')
k = 3;
else if (tokens[9][c] == 'T' || tokens[9][c] == 't')
k = 4;
seq_b.push_back(k);
if (have_quality_scores) {
// cout << int(qual_s[c] - qual_subtract) <<':'<<pow(10,-
// int(qual_s[c] - qual_subtract)/10.0)<<' '<<flush; //newCout
int q =
int(41 * (1 - pow(10, -int(qual_s[c] - qual_subtract) / 10.0)) +
qual_subtract);
qual_s[c] = char(q);
used_qual.append(1, qual_s[c]);
} else
used_qual.append(1, 'I');
// cout<< tokens[9][c];
c++;
}
else if (symbols[i] == 'I')
for (int j = 0; j < sub_length_vec[i]; j++) {
// cout<< tokens[9][c];
c++;
// indels++;
}
else if (symbols[i] == 'D')
for (int j = 0; j < sub_length_vec[i]; j++) {
used_qual.append(1, gap_quality);
seq_b.push_back(0);
indels++;
// cout<< '*';
}
else if (symbols[i] == 'P') {
// for(int j =0; j< sub_length_vec[i];j++){
// cout<< '*';
// }
}
}
return 0;
}
phaplo::ParseStatistics
parseSAMpaired(string al, double max_gap_fraction,
double min_align_score_fraction, double min_qual, int min_length,
char gap_quality, double &mean_length, int &min_seq_start,
int &max_seq_start, int &max_sequence_stop,
int &min_sequence_stop, bool &have_quality_scores,
vector<string> &quality_scores, vector<int> &strand,
vector<vector<int>> &Reads, vector<int> &Positions_Start,
vector<string> &IDs) {
string line;
have_quality_scores = true;
ifstream inf6(al.c_str(), ios::in);
vector<int> seq_b, seq_b_pairs;
string used_qual, used_qual_pairs;
double a_score, a_score_pairs;
int al_start, al_start_pairs;
int indels, indels_pairs;
auto statistics = phaplo::ParseStatistics();
std::unordered_map<std::string, std::vector<std::string>> candidates;
while (getline(inf6, line, '\n')) {
if (line[0] == '@') {
continue;
}
statistics.reads.total_count++;
const auto tokens = tokenize(line, "\t");
const auto RC = static_cast<unsigned int>(atoi(tokens[1].c_str()));
const auto id = tokens[0];
const auto sRC = phaplo::binary(RC);
if (phaplo::used_bits(sRC) > 8) {
statistics.reads.unsupported_attribute++;
continue;
}
const auto is_unmapped = bool{sRC[2] || sRC[3]};
const auto is_paired_read = sRC[0];
if (is_unmapped) {
statistics.reads.unmapped++;
continue;
}
if (!is_paired_read) {
statistics.reads.unpaired++;
continue;
}
const auto found = candidates.find(id);
if (found != candidates.end()) {
const auto is_read1 = bool{sRC[6]};
const auto tokens_1 =
is_read1 ? std::move(tokens) : std::move(found->second);
const auto tokens_2 =
!is_read1 ? std::move(tokens) : std::move(found->second);
candidates.erase(found);
statistics.pairs.total_count++;
parse_sam_line(tokens_1, used_qual, seq_b, a_score, gap_quality, indels,
have_quality_scores, al_start);
parse_sam_line(tokens_2, used_qual_pairs, seq_b_pairs, a_score_pairs,
gap_quality, indels_pairs, have_quality_scores,
al_start_pairs);
if (!(seq_b.size() > min_length && seq_b_pairs.size() > min_length)) {
statistics.pairs.sequence_too_short++;
continue;
}
if (!(double(indels) / seq_b.size() < max_gap_fraction &&
double(indels_pairs) / seq_b_pairs.size() < max_gap_fraction)) {
statistics.pairs.gap_fraction_too_high++;
continue;
}
if (!((std::isnan(a_score) ||
a_score / seq_b.size() > min_align_score_fraction) &&
(std::isnan(a_score_pairs) ||
a_score_pairs / seq_b_pairs.size() > min_align_score_fraction))) {
statistics.pairs.align_score_fraction_too_low++;
continue;
}
int StartPos = al_start;
vector<int> SEQ_combined = seq_b;
string Qscores = used_qual;
bool is_gap = false;
int Nlength = 0;
if (al_start_pairs == al_start) {
// of_gaps << 0<<',';
}
if (al_start_pairs < al_start) {
StartPos = al_start_pairs;
is_gap = false;
if (al_start - (al_start_pairs + (int)seq_b_pairs.size()) > 0) {
is_gap = true;
}
if (is_gap) {
Nlength = al_start - (al_start_pairs + (int)seq_b_pairs.size());
vector<int> Ns(Nlength, 7);
seq_b_pairs.insert(seq_b_pairs.end(), Ns.begin(), Ns.end());
seq_b_pairs.insert(seq_b_pairs.end(), seq_b.begin(), seq_b.end());
SEQ_combined = seq_b_pairs;
string Nstr(Nlength, '_');
Qscores = used_qual_pairs + Nstr + used_qual;
} else {
// of_gaps << 0<<',';
vector<int> first_part(seq_b_pairs.begin(),
seq_b_pairs.begin() +
(al_start - al_start_pairs));
first_part.insert(first_part.end(), seq_b.begin(), seq_b.end());
SEQ_combined = first_part;
Qscores =
used_qual_pairs.substr(0, al_start - al_start_pairs) + used_qual;
}
} else {
if (al_start_pairs > al_start) {
is_gap = false;
if (al_start_pairs - (al_start + (int)seq_b.size()) > 0) {
is_gap = true;
}
if (is_gap) {
Nlength = al_start_pairs - (al_start + (int)seq_b.size());
// of_gaps << Nlength<<',';
vector<int> Ns(Nlength, 7);
seq_b.insert(seq_b.end(), Ns.begin(), Ns.end());
seq_b.insert(seq_b.end(), seq_b_pairs.begin(), seq_b_pairs.end());
SEQ_combined = seq_b;
string Nstr(Nlength, 'Z');
Qscores = used_qual + Nstr + used_qual_pairs;
} else {
// of_gaps << 0<<',';
vector<int> first_part(seq_b.begin(),
seq_b.begin() + (al_start_pairs - al_start));
first_part.insert(first_part.end(), seq_b_pairs.begin(),
seq_b_pairs.end());
SEQ_combined = first_part;
Qscores = used_qual.substr(0, al_start_pairs - al_start) +
used_qual_pairs;
}
}
}
if (!is_gap || Nlength < 200) {
Positions_Start.push_back(StartPos);
Reads.push_back(SEQ_combined);
IDs.push_back(id);
mean_length += SEQ_combined.size();
string qNeqStr(SEQ_combined.size(), 'Z');
quality_scores.push_back(Qscores);
strand.push_back(RC);
if (al_start + seq_b.size() > max_sequence_stop)
max_sequence_stop = al_start + seq_b.size();
if (al_start + seq_b.size() < min_sequence_stop)
min_sequence_stop = al_start + seq_b.size();
if (al_start < min_seq_start)
min_seq_start = al_start;
if (al_start > max_seq_start)
max_seq_start = al_start;
}
} else {
candidates[id] = tokens;
}
}
return statistics;
}
int MultiNomialDPMReadsSemiEntropy(
const int &nSample,
const vector<vector<int>> &Reads,
const vector<string> &quality_scores, const vector<int> &Positions_Start,
const vector<int> &reads_in_window,
vector<vector<double>> Assignments, // supervision information
const int &WindowStart, const int &WindowStop,
const int &K,
const double &alphaMN, const double &alpha,
Matrix<double> &MNprob, //(GD,K*levels) log-scale (+1e-6)
Matrix<double> &MNprobMEAN,
Matrix<int> &maxTab, // (GD,K);
Matrix<double> &pi, //(K,1)
Matrix<double> &piMean, vector<int> &C,
Matrix<int> &CMAX, // (n,K)
const int &lseed, const double &burnin_fraction,
const vector<int> &refSeq,
const vector<int> &entropy_select,
bool local) {
int n = reads_in_window.size();
int GD = WindowStop - WindowStart + 1;
int burnin = int(nSample * burnin_fraction);
int idx, idx_global;
vector<int> C_old(n, 0);
mersenne myrng;
myrng.initialize(lseed);
piMean = 0;
MNprobMEAN = 0;
CMAX = 0;
Matrix<double> alphaProb(K, 1);
vector<double> bins(K);
Matrix<double> cprobL(K, 1);
Matrix<double> cprob(K, n);
// vector<double> logPi(K,log(1.0/K));
Matrix<double> logPi(n, K);
for (int j = 0; j < n; j++) {
for (int k = 0; k < K; k++) {
logPi(j, k) = log(1.0 / K);
}
}
vector<int> nK_vec(K, 0);
vector<long> lev(levels, 0);
vector<vector<long>> GD_lev(GD, lev);
vector<vector<vector<long>>> counts(K, GD_lev); // dims = (K,GD,levels);
Matrix<double> probs(GD, levels);
Matrix<double> tot_counts(GD, levels);
Matrix<double> diriMN(levels, 1);
for (int j = 0; j < n; j++) {
nK_vec[C[j]] += 1;
}
int nuc;
// add one "reference" read...
for (int k = 0; k < K; k++) {
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 1) {
nuc = refSeq[l];
if (nuc < 5)
counts[k][l][nuc] += 1;
}
}
nK_vec[k] += 1;
}
vector<int> is_clamped(n, 0);
int qual;
for (int o = 0; o < n; o++) {
for (int r = 0; r < Reads[reads_in_window[o]].size(); r++) {
idx_global = r + Positions_Start[reads_in_window[o]] - 1;
idx = r + Positions_Start[reads_in_window[o]] - WindowStart;
if (Positions_Start[reads_in_window[o]] - WindowStart >
local_window_size &&
Positions_Start[reads_in_window[o]] +
Reads[reads_in_window[o]].size() - WindowStart <
GD - local_window_size)
is_clamped[o] = 1;
if (idx >= 0 && idx < GD && entropy_select[idx_global] == 1) {
nuc = Reads[reads_in_window[o]][r];
qual = int(quality_scores[reads_in_window[o]][r]) - qual_subtract;
// cout << qual <<' '<< flush;
if (!include_deletions && nuc == 0)
continue;
if (nuc < 5) {
counts[C[o]][idx][nuc] += qual;
}
}
}
}
cout << "Now running MCMC iterations... " << endl;
for (int i = 0; i < nSample; i++) {
C_old = C;
for (int k = 0; k < K; k++) {
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 1) {
for (int m = 0; m < levels; m++)
probs(l, m) = counts[k][l][m] + 1e-3;
probs(l, _) = probs(l, _) * nK_vec[k] / sum(probs(l, _));
// if( nK_vec[k] == 1)
// cout << k <<' '<< probs(l,_)<<' '<< nK_vec[k] << endl;
}
}
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 1) {
diriMN = myrng.rdirich(t(probs(l, _)) + alphaMN);
for (int p = 0; p < levels; p++) {
if (i > burnin)
MNprobMEAN(l, k * levels + p) =
MNprobMEAN(l, k * levels + p) + diriMN[p];
MNprob(l, k * levels + p) = log(diriMN[p]);
}
}
}
}
for (int o = 0; o < n; o++) {
if (is_clamped[o])
continue;
vector<double> lMN(K, 0);
for (int r = 0; r < Reads[reads_in_window[o]].size(); r++) {
idx_global = r + Positions_Start[reads_in_window[o]] - 1;
idx = r + Positions_Start[reads_in_window[o]] - WindowStart;
if (idx >= 0 && idx < GD && entropy_select[idx_global] == 1) {
nuc = Reads[reads_in_window[o]][r];
if (!include_deletions && nuc == 0)
continue;
if (nuc < 5)
for (int k = 0; k < K; k++)
lMN[k] += MNprob(idx, k * levels + nuc);
}
}
for (int k = 0; k < K; k++)
cprob(k, o) = lMN[k] + logPi(o, k);
}
for (int l = 0; l < n; l++) {
if (is_clamped[l]) {
if (i > burnin) {
CMAX(l, C[l]) += 1;
}
continue;
}
double lMax = max(cprob(_, l));
cprobL = exp(cprob(_, l) - lMax);
cprobL = cprobL / sum(cprobL);
bins[0] = cprobL[0];
for (int k = 1; k < K; k++) {
bins[k] = bins[k - 1] + cprobL[k];
}
double unif = myrng.runif() * bins[K - 1];
bins[K - 1] += 1e-6;
for (int k = 0; k < K; k++) {
if (unif < bins[k]) {
C[l] = k;
if (i > burnin) {
CMAX(l, k) += 1;
}
if (C[l] != C_old[l]) {
for (int r = 0; r < Reads[reads_in_window[l]].size(); r++) {
idx_global = r + Positions_Start[reads_in_window[l]] - 1;
idx = r + Positions_Start[reads_in_window[l]] - WindowStart;
if (idx >= 0 && idx < GD && entropy_select[idx_global] == 1) {
nuc = Reads[reads_in_window[l]][r];
qual =
int(quality_scores[reads_in_window[l]][r]) - qual_subtract;
if (!include_deletions && nuc == 0)
continue;
if (nuc < 5) {
// downdate couts in C_old[l]
counts[C_old[l]][idx][nuc] -= qual;
// update couts in C[l]
counts[C[l]][idx][nuc] += qual;
}
}
}
nK_vec[C_old[l]] -= 1;
nK_vec[C[l]] += 1;
}
break;
}
}
}
if (local) {
for (int k = 0; k < K; k++) {
alphaProb[k] = alpha / K + nK_vec[k];
}
pi = myrng.rdirich(alphaProb);
for (int l = 0; l < n; l++) {
if (is_clamped[l]) {
continue;
}
for (int k = 0; k < K; k++) {
logPi(l, k) = log(pi[k]);
}
}
} else {
for (int l = 0; l < n; l++) {
if (is_clamped[l]) {
continue;
}
for (int k = 0; k < K; k++) {
alphaProb[k] = alpha * Assignments[l][k] + nK_vec[k];
}
pi = myrng.rdirich(alphaProb);
for (int k = 0; k < K; k++) {
logPi(l, k) = log(pi[k]);
}
}
}
if (i > burnin) {
piMean = piMean + pi;
}
if (i % 50 == 0) {
cout << i << ' ' << flush;
}
} // end sampling with entropy thresholding
cout << endl;
// recompute MNprobMEAN for positions not selected
for (int k = 0; k < K; k++)
counts[k] = GD_lev;
for (int o = 0; o < n; o++) {
for (int r = 0; r < Reads[reads_in_window[o]].size(); r++) {
idx_global = r + Positions_Start[reads_in_window[o]] - 1;
idx = r + Positions_Start[reads_in_window[o]] - WindowStart;
if (idx >= 0 && idx < GD && entropy_select[idx_global] == 0) {
nuc = Reads[reads_in_window[o]][r];
qual = int(quality_scores[reads_in_window[o]][r]) - qual_subtract;
if (!include_deletions && nuc == 0)
continue;
if (nuc < 5) {
counts[C[o]][idx][nuc] += qual;
}
}
}
}
// add one "reference" read...
for (int k = 0; k < K; k++) {
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 0) {
nuc = refSeq[l];
if (nuc < 5)
counts[k][l][nuc] += 1;
}
}
}
for (int k = 0; k < K; k++) {
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 0) {
for (int m = 0; m < levels; m++)
probs(l, m) = counts[k][l][m] + 1e-3;
probs(l, _) = probs(l, _) / sum(probs(l, _));
for (int p = 0; p < levels; p++) {
MNprobMEAN(l, k * levels + p) = probs(l, p);
}
}
}
}
for (int k = 0; k < K; k++) {
piMean[k] = piMean[k] / (nSample - burnin + 1);
}
for (int l = 0; l < GD; l++) {
idx_global = l + WindowStart - 1;
if (entropy_select[idx_global] == 1) {
MNprobMEAN(l, _) = MNprobMEAN(l, _) / (nSample - burnin + 1);
}
for (int k = 0; k < K; k++) {
double mt = -1e100;
for (int p = 0; p < levels; p++) {
if (MNprobMEAN(l, k * levels + p) > mt) {
mt = MNprobMEAN(l, k * levels + p);
maxTab(l, k) = p;
}
}
}
}
return 0;
}
/**
* @return The path to the FASTA file that the reconstructed haplotypes were
* written to.
*/
std::string visualizeAlignments(
int K, int WindowStart, int GD, const vector<int> &Positions_Start,
const vector<vector<int>> &Reads, const vector<int> &reads_in_window,
const vector<string> &IDs_in_window, const vector<string> &IDs,
const vector<int> &strand, string prefix, string SQ_string,
const vector<int> &entropy_selection, const vector<int> &C_index,
const vector<int> &L, const Matrix<int> &maxTab, bool have_true_haplotypes,
const vector<vector<int>> &Haplos, vector<int> Haplo_Positions_Start,
const vector<string> &trueHaplo_IDs,
const vector<vector<int>> &reconstructedHaplos,
const vector<vector<double>> &reconstructedConf, vector<int> &match,
vector<int> &match_costs, bool make_pgm,
vector<double> reconstructedFrequency,
const vector<vector<int>> &reconstructed_overlaps, bool plot_reads,
const vector<vector<int>> &error_positions) {
bool SNV_only = true;
if (visualization_level > 1)
SNV_only = false;
if (visualization_level > 0) {
vector<bool> is_SNV(GD);
if (have_true_haplotypes) {
for (int i = 0; i < GD; i++) {
vector<int> hk(Haplos.size(), 0);
for (int k = 0; k < Haplos.size(); k++) {
int iH = i + WindowStart - Haplo_Positions_Start[k];
if (iH >= 0 && iH < Haplos[k].size())
hk[k] = Haplos[k][iH];
}
sort(hk.begin(), hk.end());
vector<int>::iterator new_end = unique(hk.begin(), hk.end());
hk.erase(new_end, hk.end());
is_SNV[i] = false;
if (hk.size() > 1) {
is_SNV[i] = true;
}
}
}
ostringstream sVis1;
sVis1 << prefix << "visuAlign_" << WindowStart << "_"
<< WindowStart + GD - 1 << ".html";
string sVis = sVis1.str();
ofstream ofAW(sVis.c_str(), ios::out);
ofAW << fixed << setprecision(4);
ofAW << "<html lang=\"eng\" xml:lang=\"eng\" "
"xmlns=\"http://www.w3.org/1999/xhtml\">"
<< endl;
ofAW << "<head>" << endl;
ofAW << "<link href=\"nuc-colors.css\" rel=\"stylesheet\" "
"type=\"text/css\" />"
<< endl;
ofAW << "</head>" << endl;
ofAW << "<body id=\"top\">" << endl;
ofAW << "<pre id=\"alignmentContent\" xml:space=\"preserve\" "
"style=\"cursor:default\">"
<< endl;
// consensus: entropy_selection
ofAW << '\t';
for (int i = 0; i < GD; i++) {
if (SNV_only) {
if (!entropy_selection[i + WindowStart - 1] && !is_SNV[i])
continue;
}
char nuc = SQ_string[i + WindowStart - 1];
if (entropy_selection[i + WindowStart - 1]) {
ofAW << "<span class=\"entropySel\">" << nuc << "</span>";
} else
ofAW << nuc;
}
ofAW << '\t' << "reference: entropy_selection" << '\t';
ofAW << endl;
if (have_true_haplotypes) {
// consensus: true mutations
ofAW << '\t';
for (int i = 0; i < GD; i++) {
if (SNV_only) {
if (!entropy_selection[i + WindowStart - 1] && !is_SNV[i])
continue;
}
vector<int> hk(Haplos.size(), 0);
for (int k = 0; k < Haplos.size(); k++) {
int iH = i + WindowStart - Haplo_Positions_Start[k];
if (iH >= 0 && iH < Haplos[k].size())
hk[k] = Haplos[k][iH];
}
sort(hk.begin(), hk.end());
vector<int>::iterator new_end = unique(hk.begin(), hk.end());
hk.erase(new_end, hk.end());
bool is_true = false;
if (hk.size() > 1) {
is_true = true;
}
char nuc = SQ_string[i + WindowStart - 1];
if (is_true) {
ofAW << "<span class=\"trueMut\">" << nuc << "</span>";
} else
ofAW << nuc;
}
ofAW << '\t' << "reference: true mutations" << '\t';
ofAW << endl;
ofAW << '\t';
for (int i = 0; i < GD; i++) {
if (SNV_only) {
if (!entropy_selection[i + WindowStart - 1] && !is_SNV[i])
continue;
}
ofAW << "-";
}
ofAW << '\t';
ofAW << endl;
}
// the consensus:
ofAW << '\t';
for (int i = 0; i < GD; i++) {
if (SNV_only) {
if (!entropy_selection[i + WindowStart - 1] && !is_SNV[i])
continue;
}
char nuc = SQ_string[i + WindowStart - 1];
if (nuc == '-') {
ofAW << "<a title=\"" << i + WindowStart << "\">-</a>";
} else {
if (nuc == 'A') {
ofAW << "<span class=\"A\"><a title=\"" << i + WindowStart
<< "\">A</a></span>";
} else {
if (nuc == 'C') {
ofAW << "<span class=\"C\"><a title=\"" << i + WindowStart
<< "\">C</a></span>";
} else {
if (nuc == 'G') {
ofAW << "<span class=\"G\"><a title=\"" << i + WindowStart
<< "\">G</a></span>";
} else {
if (nuc == 'T') {
ofAW << "<span class=\"T\"><a title=\"" << i + WindowStart
<< "\">T</a></span>";
} else {
if (nuc == 'N') {
ofAW << "<span class=\"N\"><a title=\"" << i + WindowStart
<< "\">N</a></span>";
} else {
ofAW << "<a title=\"" << i + WindowStart << "\">.</a>";
}
}
}
}
}
}
}
ofAW << '\t' << "reference" << '\t' << endl;
ofAW << '\t';
for (int i = 0; i < GD; i++) {
if (SNV_only) {
if (!entropy_selection[i + WindowStart - 1] && !is_SNV[i])
continue;
}
ofAW << "-";
}