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KmersStatistics.cpp
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Tue, Aug 20, 10:11

KmersStatistics.cpp
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#include <KmersStatistics.hpp>
#include <string>
#include <vector>
#include <unordered_map>
#include <Matrix2D.hpp>
#include <Matrix3D.hpp>
#include <Statistics.hpp> // poisson_cdf()
#include <dna_utility.hpp> // dna::consensus_to_kmer()
Matrix2D<int> kmers::compute_mononucleotide(size_t k,
const std::vector<std::string>& kmers,
const std::vector<int>& counts)
{
Matrix2D<int> comp(k, 4, 0) ;
for(size_t i=0; i<kmers.size(); i++)
{
std::string kmer = kmers[i] ;
int count = counts[i] ;
for (size_t j=0; j<k ; j++)
{ int base = int(kmer[j]) - int('0') ;
comp(j, base) += count ;
}
}
return(comp);
}
Matrix3D<int> kmers::compute_dinucleotide(size_t k,
const std::vector<std::string>& kmers,
const std::vector<int>& counts)
{
Matrix3D<int> comp2(4, k-1, 4) ;
for(size_t i=0; i<kmers.size(); i++)
{ std::string kmer = kmers[i] ;
int count = counts[i] ;
for (size_t j=0; j<k-1; j++)
{ int base1 = int(kmer[j]) - int('0') ;
int base2 = int(kmer[j+1]) - int('0') ;
comp2(base1, j, base2) += count ;
}
}
return(comp2);
}
std::vector<double> kmers::compute_exp_values(size_t k,
const std::vector<std::string>& kmers,
const Matrix2D<int>& comp1,
const Matrix3D<int>& comp2)
{ std::vector<double> expected(kmers.size()) ;
for(size_t i=0; i<kmers.size(); i++)
{ std::string kmer = kmers[i] ;
double exp = comp1(0, int(kmer[0]) - int('0')) ;
for(size_t j=0; j<k-1; j++)
{ int base1 = int(kmer[j]) - int('0') ;
int base2 = int(kmer[j+1]) - int('0') ;
exp *= ((double)comp2(base1, j, base2) /
(double)comp1(j+1, base2)) ;
}
expected[i] = exp ;
}
return expected;
}
std::vector<double> kmers::compute_pvalues(const std::vector<int>& counts,
const std::vector<double>& expected)
{
std::vector<double> pvalues(counts.size()) ;
for(size_t i=0; i<counts.size(); i++)
{
pvalues[i] = poisson_cdf((double)counts[i],
expected[i],
false) ;
}
return pvalues ;
}
std::unordered_map<std::string, int>
kmers::compute_kmer_count(const Matrix2D<int>& sequences,
size_t length)
{ std::unordered_map<std::string, int> kmer_count ;
size_t n_row = sequences.get_dim()[0] ;
size_t n_shift = sequences.get_dim()[1] - length + 1 ;
for(size_t i=0; i<n_row; i++)
{ for(size_t s=0; s<n_shift; s++)
{ // get kmer
std::string kmer(length, '0') ;
bool n_found = false ;
for(size_t j_seq=s, j_kmer=0; j_seq<s+length; j_seq++, j_kmer++)
{ // check for N's
if(sequences(i, j_seq) == dna::char_to_int('N'))
{ n_found = true ;
break ;
}
kmer[j_kmer] = std::to_string(sequences(i, j_seq))[0] ;
}
// found N -> skip this one
if(n_found)
{ continue ; }
// insert
auto iter = kmer_count.find(kmer) ;
if(iter == kmer_count.end())
{ kmer_count.emplace(kmer, 1) ; }
// update
else
{ iter->second += 1 ; }
}
}
return kmer_count ;
}
std::unordered_map<std::string, int>
kmers::compute_kmer_count(const Matrix3D<double>& consensus_sequence,
size_t length)
{ std::unordered_map<std::string, int> kmer_count ;
size_t n_row = consensus_sequence.get_dim()[0] ;
size_t n_shift = consensus_sequence.get_dim()[1] - length + 1 ;
for(size_t i=0; i<n_row; i++)
{ for(size_t s=0; s<n_shift; s++)
{ // get kmer
std::string kmer = dna::consensus_to_kmer(consensus_sequence,
i,
s,
s+length) ;
// insert
auto iter = kmer_count.find(kmer) ;
if(iter == kmer_count.end())
{ kmer_count.emplace(kmer, 1) ; }
// update
else
{ iter->second += 1 ; }
}
}
return kmer_count ;
}
std::pair<std::vector<std::string>, std::vector<double>>
kmers::compute_kmer_pvalue(const Matrix2D<int>& sequences,
size_t k)
{
// get kmer count
std::unordered_map<std::string, int> kmer_count =
kmers::compute_kmer_count(sequences, k) ;
// turn to vectors
std::vector<std::string> kmers(kmer_count.size()) ;
size_t i=0 ;
for(const auto& iter : kmer_count)
{ kmers[i] = iter.first ;
i++ ;
}
std::sort(kmers.begin(), kmers.end()) ;
std::vector<int> counts(kmer_count.size()) ;
i=0 ;
for(const auto& kmer : kmers)
{ counts[i] = kmer_count[kmer] ;
i++ ;
}
kmer_count.clear() ;
// get mononucleotide composition in kmer
// this is a probability matrix modeling the kmer
Matrix2D<int> comp1 = kmers::compute_mononucleotide(k, kmers, counts) ;
// get dinucleotide composition in kmer
// this is a probability matrix modeling the kmer
Matrix3D<int> comp2 = kmers::compute_dinucleotide(k, kmers, counts) ;
// get expected number of occurence for each kmer
std::vector<double> expected = kmers::compute_exp_values(k,
kmers,
comp1,
comp2) ;
// compute the pvalue associated to each kmer
std::vector<double> pvalues = kmers::compute_pvalues(counts,
expected) ;
return std::make_pair(kmers, pvalues) ;
}
std::pair<std::vector<std::string>, std::vector<double>>
kmers::compute_kmer_pvalue(const Matrix3D<double>& consensus_sequences,
size_t k)
{
// get kmer count
std::unordered_map<std::string, int> kmer_count =
kmers::compute_kmer_count(consensus_sequences, k) ;
/*
consensus_sequences.get_dim() ;
std::unordered_map<std::string, int> kmer_count ;
// ANN
kmer_count["000"] = 1 ;
kmer_count["001"] = 2 ;
kmer_count["002"] = 3 ;
kmer_count["003"] = 4 ;
kmer_count["010"] = 5 ;
kmer_count["011"] = 6 ;
kmer_count["012"] = 7 ;
kmer_count["013"] = 8 ;
kmer_count["020"] = 9 ;
kmer_count["021"] = 10 ;
kmer_count["022"] = 11 ;
kmer_count["023"] = 12 ;
kmer_count["030"] = 13 ;
kmer_count["031"] = 14 ;
kmer_count["032"] = 15 ;
kmer_count["033"] = 16 ;
// CNN
kmer_count["100"] = 1 ;
kmer_count["101"] = 2 ;
kmer_count["102"] = 3 ;
kmer_count["103"] = 4 ;
kmer_count["110"] = 5 ;
kmer_count["111"] = 6 ;
kmer_count["112"] = 7 ;
kmer_count["113"] = 8 ;
kmer_count["120"] = 9 ;
kmer_count["121"] = 10 ;
kmer_count["122"] = 11 ;
kmer_count["123"] = 12 ;
kmer_count["130"] = 13 ;
kmer_count["131"] = 14 ;
kmer_count["132"] = 15 ;
kmer_count["133"] = 16 ;
// GNN
kmer_count["200"] = 1 ;
kmer_count["201"] = 2 ;
kmer_count["202"] = 3 ;
kmer_count["203"] = 4 ;
kmer_count["210"] = 5 ;
kmer_count["211"] = 6 ;
kmer_count["212"] = 7 ;
kmer_count["213"] = 8 ;
kmer_count["220"] = 9 ;
kmer_count["221"] = 10 ;
kmer_count["222"] = 11 ;
kmer_count["223"] = 12 ;
kmer_count["230"] = 13 ;
kmer_count["231"] = 14 ;
kmer_count["232"] = 15 ;
kmer_count["233"] = 16 ;
// TNN
kmer_count["300"] = 1 ;
kmer_count["301"] = 2 ;
kmer_count["302"] = 3 ;
kmer_count["303"] = 4 ;
kmer_count["310"] = 5 ;
kmer_count["311"] = 6 ;
kmer_count["312"] = 7 ;
kmer_count["313"] = 8 ;
kmer_count["320"] = 9 ;
kmer_count["321"] = 10 ;
kmer_count["322"] = 11 ;
kmer_count["323"] = 12 ;
kmer_count["330"] = 13 ;
kmer_count["331"] = 14 ;
kmer_count["332"] = 15 ;
kmer_count["333"] = 16 ;
*/
// turn to vectors
std::vector<std::string> kmers(kmer_count.size()) ;
size_t i=0 ;
for(const auto& iter : kmer_count)
{ kmers[i] = iter.first ;
i++ ;
}
std::sort(kmers.begin(), kmers.end()) ;
std::vector<int> counts(kmer_count.size()) ;
i=0 ;
for(const auto& kmer : kmers)
{ counts[i] = kmer_count[kmer] ;
i++ ;
}
kmer_count.clear() ;
// get mononucleotide composition in kmer
// this is a probability matrix modeling the kmer
Matrix2D<int> comp1 = kmers::compute_mononucleotide(k, kmers, counts) ;
// get dinucleotide composition in kmer
// this is a probability matrix modeling the kmer
Matrix3D<int> comp2 = kmers::compute_dinucleotide(k, kmers, counts) ;
// get expected number of occurence for each kmer
std::vector<double> expected = kmers::compute_exp_values(k,
kmers,
comp1,
comp2) ;
// compute the pvalue associated to each kmer
std::vector<double> pvalues = kmers::compute_pvalues(counts,
expected) ;
return std::make_pair(kmers, pvalues) ;
}

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