Ludovic PLATON / IRSOM

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Ludovic PLATON
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Showing 115 changed files with 932 additions and 218 deletions
......@@ -44,6 +44,7 @@ namespace import {
*/
class Fasta : public multithread::Buffer_producer<data::Data_basic*>{
std::string file;
unsigned int length_min;
protected:
void work_out(){
std::cout << "Start fasta reader : " << this->file << std::endl;
......@@ -59,7 +60,7 @@ namespace import {
int idx(1);
while(std::getline(input,line).good()){
if(line.empty() || line[0] == '>'){
if(!id.empty()){
if(!id.empty() && sequence.size() > this->length_min){
this->push(new entry::Sequence(id,sequence));
idx ++;
}
......@@ -80,7 +81,7 @@ namespace import {
}
}
}
if( !id.empty() ){
if(!id.empty() && sequence.size() > this->length_min){
this->push(new entry::Sequence(id,sequence));
}
std::cout << "End fasta reader" << std::endl;
......@@ -94,7 +95,7 @@ namespace import {
* @param buffer_size a int, the size of the internal buffer
*/
Fasta(multithread::Buffer<data::Data_basic*> *out,std::string file):
multithread::Buffer_producer<data::Data_basic*>(out),file(file){}
multithread::Buffer_producer<data::Data_basic*>(out),file(file),length_min(5){}
};
}
......
......@@ -4,8 +4,7 @@
#include <chrono>
const int THREAD_NB = 4;
const std::chrono::milliseconds THREAD_SLEEP(2);
const int BUFFER_SIZE = 500;
const int NB_ELT_BUF = 100;
const std::chrono::milliseconds THREAD_SLEEP(1);
const int BUFFER_SIZE = 100;
#endif // CONSTANT_H
......
......@@ -7,6 +7,7 @@
#include "feature.h"
#include "feature_callable.h"
#include "util.h"
namespace feature {
const std::vector<std::string> ORF::START{"AUG","ATG","TTG","UUG","CTG","CUG"};
......@@ -125,7 +126,17 @@ std::string ORF::to_dict(){
}
std::string ORF::to_csv(){
return this->name+","+std::to_string(this->length) + ","+ std::to_string(this->coverage) + "," + std::to_string(this->start_mean) + "," + std::to_string(this->start_std) + "," + std::to_string(this->end_mean) + "," + std::to_string(this->end_std);
return this->name+"," +
std::to_string(this->length) + "," +
std::to_string(this->coverage) + "," +
std::to_string(this->coverage_all_mean) + "," +
std::to_string(this->coverage_all_std) + "," +
std::to_string(this->frame_biases) + "," +
std::to_string(this->frequency) + "," +
std::to_string(this->start_mean) + "," +
std::to_string(this->start_std) + "," +
std::to_string(this->end_mean) + "," +
std::to_string(this->end_std);
}
std::string CodonBiases::to_dict(){
......@@ -149,7 +160,8 @@ std::string CodonBiases::to_csv(){
std::to_string(this->composition[0]) + ","+
std::to_string(this->composition[1]) + ","+
std::to_string(this->composition[2]) + ","+
std::to_string(this->composition[3]);
std::to_string(this->composition[3])+ ","+
std::to_string(this->gc_content);
}
// Constructor
......@@ -157,7 +169,7 @@ std::string CodonBiases::to_csv(){
// Kmer
Kmer::Kmer(entry::Sequence &s, const unsigned int &k):Feature(s.getName()),k(k){
std::string seq = s.getSeq();
for(unsigned int i(0); i < s.getLength() - this->k - 1; i++){
for(unsigned int i(0); i < s.getLength() - this->k + 1; i++){
std::string kmer = seq.substr(i,this->k);
std::transform(kmer.begin(),kmer.end(),kmer.begin(),::toupper);
std::replace(kmer.begin(),kmer.end(),'U','T');
......@@ -180,7 +192,7 @@ ORF::ORF(const entry::Sequence &s):Feature(s.getName()),length(0){
std::transform(seq.begin(),seq.end(),seq.begin(),toupper);
std::vector<unsigned int> start_codon;
std::vector<unsigned int> end_codon;
std::pair<unsigned int, unsigned int> orf_pair;
std::vector<std::tuple<unsigned int, unsigned int, unsigned int, unsigned int>> orfs;
//find position codon start and stop
for(unsigned int i(0); i < seq.length(); i++){
std::string tmp = seq.substr(i,3);
......@@ -200,9 +212,10 @@ ORF::ORF(const entry::Sequence &s):Feature(s.getName()),length(0){
for(auto const &end_pos: end_codon){
if(start_pos < end_pos){
unsigned int length = end_pos - start_pos;
unsigned int frame = start_pos % 3;
if((length%3 == 0)){
orfs.push_back(std::tuple<unsigned int, unsigned int, unsigned int, unsigned int>(frame,length,start_pos,end_pos));
if(length > this->length){
orf_pair = std::pair<unsigned int, unsigned int>(start_pos,end_pos);
this->length = length;
}
break;
......@@ -211,33 +224,38 @@ ORF::ORF(const entry::Sequence &s):Feature(s.getName()),length(0){
}
}
}
this->coverage = static_cast<float>(this->length) / s.getLength();
this->length = log10(this->length);
this->start_mean = 0;
for(auto const &start_pos: start_codon){
this->start_mean += start_pos;
}
this->start_mean /= start_codon.size();
this->start_std = 0;
for(auto const &start_pos: start_codon){
this->start_std += pow(start_pos - this->start_mean,2.0);
}
this->start_std = sqrt(this->start_mean)/start_codon.size() / s.getLength();
this->start_mean /= s.getLength();
this->end_mean = 0;
for(auto const &end_pos: end_codon){
this->end_mean += end_pos;
}
this->end_mean /= end_codon.size();
if(orfs.size() > 0){
int orf_per_frame[3] = {0,0,0};
for(auto const &orf: orfs){
this->coverage_all_mean += static_cast<float>(std::get<1>(orf))/s.getLength();
orf_per_frame[std::get<0>(orf)] += 1;
this->start_mean += std::get<2>(orf);
this->end_mean += std::get<3>(orf);
}
this->start_mean = this->start_mean / orfs.size() / s.getLength();
this->end_mean = this->end_mean / orfs.size() /s.getLength();
this->coverage_all_mean = this->coverage_all_mean / orfs.size();
this->frequency = static_cast<float>(orfs.size())/start_codon.size();
auto mm = std::minmax_element(std::begin(orf_per_frame),std::end(orf_per_frame));
this->frame_biases = 1.0 - static_cast<float>(*mm.first)/
static_cast<float>(*mm.second);
for(auto const &orf: orfs){
this->coverage_all_std += pow(static_cast<float>(std::get<1>(orf))/s.getLength() - this->coverage_all_mean,2.0);
this->start_std += pow(static_cast<float>(std::get<2>(orf))/s.getLength() - this->start_mean,2.0);
this->end_std += pow(static_cast<float>(std::get<3>(orf))/s.getLength() - this->end_mean,2.0);
}
this->coverage_all_std =sqrt(this->coverage_all_std / orfs.size());
this->start_std = sqrt(this->start_std / orfs.size());
this->end_std = sqrt(this->end_std / orfs.size());
this->end_std = 0;
for(auto const &end_pos: end_codon){
this->end_std += pow(end_pos - this->end_mean,2.0);
this->coverage = static_cast<float>(this->length) / s.getLength();
//this->length = exp(-static_cast<float>(this->length)/200.0);
this->length = log10(static_cast<float>(this->length));
}
this->end_std = sqrt(this->end_mean)/end_codon.size() / s.getLength();
this->end_mean /= s.getLength();
}
// Codon position
......@@ -257,6 +275,7 @@ CodonBiases::CodonBiases(const entry::Sequence &seq):Feature(seq.getName()){
case 'c':
mat[3+(i%3)] += 1;
this->composition[1] += 1;
this->gc_content += 1;
break;
case 'T':
case 't':
......@@ -269,16 +288,18 @@ CodonBiases::CodonBiases(const entry::Sequence &seq):Feature(seq.getName()){
case 'g':
mat[9+(i%3)] += 1;
this->composition[3] += 1;
this->gc_content += 1;
break;
}
}
for(int i(0); i < 4; i++){
std::valarray<int> tmp(mat[std::slice(i*3,3,1)]);
auto mm = std::minmax_element(begin(tmp),end(tmp));
auto mm = std::minmax_element(std::begin(tmp),std::end(tmp));
this->position[i] = static_cast<float>(*mm.first)/
(static_cast<float>(*mm.second) + 1.0);
this->composition[i] /= seq.getLength();
}
this->gc_content /= seq.getLength();
}
// Dist
......
......@@ -98,12 +98,17 @@ protected:
static const std::vector<std::string> END;
static bool in_start(const std::string &c);
static bool in_end(const std::string &c);
unsigned int length;
float coverage;
float start_mean;
float start_std;
float end_mean;
float end_std;
float length=0;
float coverage=0;
float coverage_all_mean=0;
float coverage_all_std=0;
float frame_biases=0;
float frequency=0;
float start_mean=0;
float start_std=0;
float end_mean=0;
float end_std=0;
/*
float start_center, start_std;
......@@ -144,6 +149,7 @@ public:
class CodonBiases: public feature::Feature{
protected:
float position[4] = {0,0,0,0};
float gc_content = 0;
float composition[4] = {0,0,0,0};
void init(const Data_basic *db);
public:
......
......@@ -55,7 +55,17 @@ int main(int argc, char* argv[])
feature::Feature_callable *kmer = static_cast<feature::Feature_callable*>(feature::Kmer::get_callable(3));
tmp_ds = new data::Data_Store_Map(output+"KMER3.txt");
ds["KMER3"] = tmp_ds;
create_feature_maker(kmer,feature::Kmer::TYPE+std::to_string(6),tmp_ds,&re);
create_feature_maker(kmer,feature::Kmer::TYPE+std::to_string(3),tmp_ds,&re);
kmer = static_cast<feature::Feature_callable*>(feature::Kmer::get_callable(4));
tmp_ds = new data::Data_Store_Map(output+"KMER4.txt");
ds["KMER4"] = tmp_ds;
create_feature_maker(kmer,feature::Kmer::TYPE+std::to_string(4),tmp_ds,&re);
kmer = static_cast<feature::Feature_callable*>(feature::Kmer::get_callable(5));
tmp_ds = new data::Data_Store_Map(output+"KMER5.txt");
ds["KMER5"] = tmp_ds;
create_feature_maker(kmer,feature::Kmer::TYPE+std::to_string(5),tmp_ds,&re);
// kmer = static_cast<feature::Feature_callable*>(feature::Kmer::get_callable(6));
// tmp_ds = new data::Data_Store_Map(output+"KMER6.txt");
......
......@@ -28,4 +28,15 @@ void tokenize(const std::string& str, ContainerT& tokens,
}
}
template <class T>
std::pair<T,T> minmax(T tab[],unsigned int size){
T min = tab[0];
T max = tab[0];
for(unsigned int i=0; i < size; i++){
min = (min > tab[i]) ? tab[i] : min;
max = (max < tab[i]) ? tab[i] : max;
}
return std::pair<T,T>(min,max);
}
#endif // UTIL_H
......
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......
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......
nb_label 2
loss_type cross_entropy
......
m 3
n 3
unit_width 4166
m 10
n 10
unit_width 1363
......
This diff could not be displayed because it is too large.
Specificity 0.9844095532950022
F1 0.9611334002006018
MCC 0.944033825472
FN 169.0
TP 3833.0
Precision 0.9645193759436336
Accuracy 0.9762379273340488
TN 8903.0
Sensitivity 0.9577711144427786
FP 141.0
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......
nb_label 2
loss_type cross_entropy
......
m 3
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unit_width 4166
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......
This diff could not be displayed because it is too large.
Sensitivity 0.8494791666666667
MCC 0.831362314807
F1 0.8936986301369862
TP 1631.0
TN 2765.0
FP 99.0
Specificity 0.9654329608938548
FN 289.0
Accuracy 0.9188963210702341
Precision 0.9427745664739884
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This diff could not be displayed because it is too large.
MCC 0.978693695598
Specificity 0.9941234084231146
F1 0.9830827067669173
TP 523.0
FN 6.0
TN 2030.0
Sensitivity 0.9886578449905482
Accuracy 0.9929988331388565
Precision 0.9775700934579439
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......
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nb_label 2
loss_type cross_entropy
......
m 3
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......
This diff could not be displayed because it is too large.
Sensitivity 0.981283861434904
Precision 0.989569536423841
MCC 0.977393223778
FP 126.0
TP 11954.0
Specificity 0.9942654287274714
FN 228.0
Accuracy 0.9896351818235053
TN 21846.0
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nb_label 2
loss_type cross_entropy
......
m 3
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......
This diff could not be displayed because it is too large.
Accuracy 0.9359705333266057
Precision 0.9487975174553918
F1 0.9204139228598307
TP 7338.0
FN 873.0
FP 396.0
Sensitivity 0.8936792108147606
TN 11212.0
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......
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......
m 3
n 3
unit_width 4166
m 10
n 10
unit_width 1363
......
This diff could not be displayed because it is too large.
FP 12.0
TP 3403.0
Specificity 0.9964093357271095
Sensitivity 0.9994126284875183
TN 3330.0
Accuracy 0.9979250037053505
FN 2.0
Precision 0.9964860907759883
MCC 0.995853906935
F1 0.9979472140762463
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......
7.905182097082329440e-01
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......
nb_label 2
loss_type cross_entropy
......
m 3
n 3
unit_width 4166
m 10
n 10
unit_width 1363
......
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MCC 0.619314779812
Sensitivity 0.7483617300131061
Precision 0.6885048231511254
Specificity 0.8879572068815961
FP 775.0
TP 1713.0
Accuracy 0.8532478818162068
TN 6142.0
FN 576.0
F1 0.7171865187356081
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