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Genotyper.hpp
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#ifndef _MOURISL_GENOTYPER
#define _MOURISL_GENOTYPER
#include <set>
#include <math.h>
#include "SeqSet.hpp"
#include "defs.h"
#include "KmerCount.hpp"
#include "SimpleVector.hpp"
#define GENETYPE_KIR 0
#define GENETYPE_HLA 1
struct _alleleInfo
{
int majorAlleleIdx ;
int geneIdx ; // which gene this allele belongs to
int alleleRank ; // -1 not select, 0-first allele, 1-second alelle
int genotypeQuality ; // assignment quality.
double abundance ;
int equivalentClass ; // the class id for the alleles with the same set of read alignment
double ecAbundance ; // the abundance for the equivalent class.
int missingCoverage ;
bool whitelist ;
} ;
struct _readGroupInfo
{
double count ; // number of reads this group contains.
} ;
struct _ecInfo
{
int length ;
int missingCoverage ;
} ; // combining the information of allels
struct _readAssignment
{
int alleleIdx ;
int start, end ;
float weight ;
float qual ;
float adjustWeight ; // The weight used for tie breaking
bool operator<(const struct _readAssignment &b) const
{
return alleleIdx < b.alleleIdx ;
}
} ;
class Genotyper
{
private:
// field type: 0 - default
// 1 - the digits up until the exon stage (before intronic stage)
void ParseAlleleName(char *allele, char *gene, char *majorAllele, int fieldsType = 0)
{
int i, j ;
int parseType = 1 ; // 1-no delimits (KIR); 2-with dimit(HLA)
int fieldsLength = alleleDigitUnits;
char delimiter = '\0' ;
strcpy(gene, allele) ;
strcpy(majorAllele, allele) ;
if (fieldsLength == -1)
{
fieldsLength = 3 ;
for (i = 0 ; allele[i] ; ++i)
if (allele[i] == ':')
{
delimiter = ':' ;
parseType = 2 ;
}
if (fieldsType == 0)
{
fieldsLength = 3 ;
}
else if (fieldsType >= 1)
{
if (parseType == 1)
fieldsLength = 5 ;
else if (parseType == 2)
fieldsLength = 3 ;
}
}
if (alleleDelimiter != '\0')
{
delimiter = alleleDelimiter ;
parseType = 2 ;
}
if (parseType == 1)
{
for (i = 0 ; allele[i] ; ++i)
{
if (allele[i] == '*')
break ;
}
gene[i] = '\0' ;
for (j = 0 ; j <= fieldsLength && allele[i + j] ; ++j)
;
majorAllele[i + j] = '\0' ;
}
else if (parseType == 2)
{
for (i = 0 ; allele[i] ; ++i)
{
if (allele[i] == '*')
break ;
}
gene[i] = '\0' ;
int k = 0 ;
for (j = i ; allele[j] ; ++j)
{
if (allele[j] == delimiter)
{
++k ;
if (k >= fieldsLength)
break ;
}
}
majorAllele[j] = '\0' ;
}
}
bool IsAlleleSameInExon(char *nameA, char *nameB)
{
char geneName[50];
char exonAlleleNameA[50];
char exonAlleleNameB[50];
ParseAlleleName(nameA, geneName, exonAlleleNameA, 1) ;
ParseAlleleName(nameB, geneName, exonAlleleNameB, 1) ;
return strcmp(exonAlleleNameA, exonAlleleNameB) == 0;
}
static bool CompSortPairByBDec( const struct _pair &p1, const struct _pair &p2 )
{
if (p1.b != p2.b)
return p2.b < p1.b ;
return p1.a < p2.a ;
}
static bool CompSortPairIntDoubleBDec( const struct _pairIntDouble &p1, const struct _pairIntDouble &p2 )
{
if (p2.b != p1.b)
return p2.b < p1.b ;
return p1.a < p2.a ;
}
static bool CompSortDoubleDec(const double &a, const double &b)
{
return b < a ;
}
bool IsAssignedReadTheSame(const std::vector<struct _pair> &l1, const std::vector<struct _pair> &l2)
{
int cnt1 = l1.size() ;
int cnt2 = l2.size() ;
int i ;
if (cnt1 != cnt2)
return false ;
// The read id in each vector should be sorted
for (i = 0 ; i < cnt1 ; ++i)
{
if (l1[i].a != l2[i].a
|| readAssignments[l1[i].a][l1[i].b].qual != readAssignments[l2[i].a][l2[i].b].qual)
return false ;
}
return true ;
}
bool IsReadAssignmentTheSame(const std::vector<struct _readAssignment> &a1, const std::vector<struct _readAssignment> &a2)
{
int cnt1 = a1.size() ;
int cnt2 = a2.size() ;
int i ;
if (cnt1 != cnt2)
return false ;
// The read id in each vector should be sorted
for (i = 0 ; i < cnt1 ; ++i)
{
if (a1[i].alleleIdx != a2[i].alleleIdx
|| a1[i].qual != a2[i].qual)
return false ;
}
return true ;
}
bool IsReadsInAlleleIdxOptimal(const std::vector<struct _pair> &readsInAllele, int k)
{
if (readAssignments[readsInAllele[k].a][readsInAllele[k].b].qual == 1)
return true ;
return false ;
}
double ReadAssignmentWeight(const struct _fragmentOverlap &o)
{
double ret = 1 ;
double similarity = o.similarity ; //o.overlap1.similarity ;
//if (o.hasMatePair && o.overlap2.similarity < similarity)
// similarity = o.overlap2.similarity ;
double segment = (1 - refSet.GetRefSeqSimilarity()) / 4.0 ;
if (segment < 0.01)
segment = 0.01;
if (similarity < 1 - 3 * segment)
ret = 0.01 ;
else if (similarity < 1 - 2 * segment)
ret = 0.1 ;
else if (similarity < 1 - segment)
ret = 0.5 ;
//else if (similarity < 1)
// ret = 0.5 ;
//if (pairedEndData && !o.hasMatePair)
// ret *= 0.9 ;
if (o.hasN)
ret /= 10.0 ;
return ret ;
}
std::string GetEquivalentClassGeneNames(const std::vector<int> &ec)
{
int i ;
int ecCnt = ec.size() ;
std::string ret("") ;
for (i = 0 ; i < ecCnt ; ++i)
{
if (i != 0)
ret += std::string(",") ;
ret += std::string(refSet.GetSeqName(ec[i])) ;
}
return ret ;
}
int Rand()
{
return randomSeed = (48271 * randomSeed) & 0x7fffffff ;
}
double alnorm ( double x, bool upper )
//****************************************************************************80
//
// Purpose:
//
// ALNORM computes the cumulative density of the standard normal distribution.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 17 January 2008
//
// Author:
//
// Original FORTRAN77 version by David Hill.
// C++ version by John Burkardt.
//
// Reference:
//
// David Hill,
// Algorithm AS 66:
// The Normal Integral,
// Applied Statistics,
// Volume 22, Number 3, 1973, pages 424-427.
//
// Parameters:
//
// Input, double X, is one endpoint of the semi-infinite interval
// over which the integration takes place.
//
// Input, bool UPPER, determines whether the upper or lower
// interval is to be integrated:
// .TRUE. => integrate from X to + Infinity;
// .FALSE. => integrate from - Infinity to X.
//
// Output, double ALNORM, the integral of the standard normal
// distribution over the desired interval.
//
{
double a1 = 5.75885480458;
double a2 = 2.62433121679;
double a3 = 5.92885724438;
double b1 = -29.8213557807;
double b2 = 48.6959930692;
double c1 = -0.000000038052;
double c2 = 0.000398064794;
double c3 = -0.151679116635;
double c4 = 4.8385912808;
double c5 = 0.742380924027;
double c6 = 3.99019417011;
double con = 1.28;
double d1 = 1.00000615302;
double d2 = 1.98615381364;
double d3 = 5.29330324926;
double d4 = -15.1508972451;
double d5 = 30.789933034;
double ltone = 7.0;
double p = 0.398942280444;
double q = 0.39990348504;
double r = 0.398942280385;
bool up;
double utzero = 18.66;
double value;
double y;
double z;
up = upper;
z = x;
if ( z < 0.0 )
{
up = !up;
z = - z;
}
if ( ltone < z && ( ( !up ) || utzero < z ) )
{
if ( up )
{
value = 0.0;
}
else
{
value = 1.0;
}
return value;
}
y = 0.5 * z * z;
if ( z <= con )
{
value = 0.5 - z * ( p - q * y
/ ( y + a1 + b1
/ ( y + a2 + b2
/ ( y + a3 ))));
}
else
{
value = r * exp ( - y )
/ ( z + c1 + d1
/ ( z + c2 + d2
/ ( z + c3 + d3
/ ( z + c4 + d4
/ ( z + c5 + d5
/ ( z + c6 ))))));
}
if ( !up )
{
value = 1.0 - value;
}
return value;
}
double EMupdate(double *ecAbundance0, double *ecAbundance1, double *ecReadCount, const std::vector<std::vector<struct _pairID> > &readGroupToAlleleEc, const SimpleVector<struct _readGroupInfo> readGroupInfo, const struct _ecInfo *ecInfo)
{
int ecCnt = equivalentClassToAlleles.size() ;
int rgCnt = readGroupToAlleleEc.size() ;
int i, j ;
// E-step: find the expected number of reads
memset(ecReadCount, 0, sizeof(double) * ecCnt) ;
for (i = 0 ; i < rgCnt ; ++i)
{
double psum = 0 ;
int size = readGroupToAlleleEc[i].size() ;
for (j = 0 ; j < size ; ++j)
{
int ecIdx = readGroupToAlleleEc[i][j].a ;
//double qual = readGroupToAlleleEc[i][j].b ;
//psum += ecAbundance0[ecIdx] / ecLength[ecIdx] * qual ;
//if (ecAbundance0[ecIdx] >= 0)
double adjust = 1.0 / (ecInfo[ecIdx].missingCoverage + 1) ;
adjust = 1 ;
psum += ecAbundance0[ecIdx] * adjust ;
}
if (psum == 0)
psum = 1 ;
for (j = 0 ; j < size ; ++j)
{
int ecIdx = readGroupToAlleleEc[i][j].a ;
//double qual = readGroupToAlleleEc[i][j].b ;
//ecReadCount[ecIdx] += readGroupInfo[i].count * (ecAbundance0[ecIdx] * qual / ecLength[ecIdx] / psum) ;
double adjust = 1.0 / (ecInfo[ecIdx].missingCoverage + 1) ;
adjust = 1 ;
ecReadCount[ecIdx] += readGroupInfo[i].count * (ecAbundance0[ecIdx] * adjust / psum) ;
}
}
// M-step: recompute the abundance
double diffSum = 0 ;
double normalization = 0 ;
for (i = 0 ; i < ecCnt ; ++i)
normalization += ecReadCount[i] / ecInfo[i].length ;
for (i = 0 ; i < ecCnt ; ++i)
{
double tmp = ecReadCount[i] / ecInfo[i].length / normalization ;
//printf("%d %s %d: %lf %lf %lf. %lf\n", i, refSet.GetSeqName(equivalentClassToAlleles[i][0]), equivalentClassToAlleles[i].size(), tmp, ecReadCount[i], ecLength[i], ecAbundance[i]) ;
diffSum += ABS(tmp - ecAbundance0[i]) ;
ecAbundance1[i] = tmp ;
}
return diffSum ;
}
// Compute the update coeffcient alpha in the SQUREEM paper
double SQUAREMalpha(double *t0, double *t1, double *t2, int n)
{
int i ;
double sqrSumR = 0 ;
double sqrSumV = 0 ;
for (i = 0 ; i < n ; ++i)
{
sqrSumR += (t1[i] - t0[i]) * (t1[i] - t0[i]) ;
sqrSumV += (t2[i] - 2 * t1[i] + t0[i]) * (t2[i] - 2 * t1[i] + t0[i]) ;
}
if (sqrSumV == 0)
return -1 ;
return -sqrt(sqrSumR) / sqrt(sqrSumV) ;
}
int readCnt ;
int totalReadCnt ;
int maxAssignCnt ;
std::vector< std::vector<struct _pair> > readsInAllele ; // a-read idx. b-the index withint the readAssignment[a]
std::vector< std::vector<struct _readAssignment> > readAssignments ; // Coalesce reads assigned to the same alleles
std::map<int, std::vector<int> > readAssignmentsFingerprintToIdx ;
std::vector< std::vector<struct _readAssignment> > allReadAssignments ;
std::vector< std::vector<int> > equivalentClassToAlleles ;
std::vector< std::vector<struct _pair> > selectedAlleles ; // a-allele name, b-which allele (0,1)
// variables for allele, majorAllele and genes
char *geneBuffer ;
char *majorAlleleBuffer ;
SimpleVector<struct _alleleInfo> alleleInfo ;
std::map<std::string, int> majorAlleleNameToIdx ;
std::map<std::string, int> geneNameToIdx ;
std::vector<int> majorAlleleSize ; // number of alleles in the major allele
std::vector<std::string> geneIdxToName ;
std::vector<std::string> majorAlleleIdxToName ;
int geneCnt ;
int majorAlleleCnt ;
int alleleCnt ;
// variables for abundance
SimpleVector<double> geneAbundance ;
SimpleVector<double> majorAlleleAbundance ;
SimpleVector<double> geneMaxMajorAlleleAbundance ;
int64_t randomSeed ;
int geneType ; // not used actually
bool pairedEndData ;
// variables for filter
double filterFrac ;
double filterCov ;
double crossGeneRate ;
double **geneSimilarity ;
int alleleDigitUnits ;
char alleleDelimiter ;
int readLength ;
public:
SeqSet refSet ;
Genotyper(int kmerLength):refSet(kmerLength)
{
geneBuffer = new char[256] ;
majorAlleleBuffer = new char[256] ;
alleleCnt = majorAlleleCnt = geneCnt = readCnt = 0 ;
maxAssignCnt = 2000 ;
randomSeed = 17 ;
geneType = GENETYPE_KIR ;
pairedEndData = true ;
filterFrac = 0.15 ;
filterCov = 1.0 ;
crossGeneRate = 0.04 ;
geneSimilarity = NULL ;
readLength = 0 ;
alleleDigitUnits = -1 ;
alleleDelimiter = '\0' ;
}
~Genotyper()
{
delete[] geneBuffer ;
delete[] majorAlleleBuffer ;
int i ;
for (i = 0 ; i < geneCnt ; ++i)
delete[] geneSimilarity[i] ;
delete[] geneSimilarity ;
}
void SetGeneType(int g)
{
geneType = g ;
}
void SetFilterFrac(double f)
{
filterFrac = f ;
}
void SetFilterCov(double c)
{
filterCov = c ;
}
void SetReadLength(int rl)
{
readLength = rl ;
}
void SetCrossGeneRate(double r)
{
crossGeneRate = r ;
}
void SetAlleleNameStructure(int n, char d)
{
alleleDigitUnits = n ;
alleleDelimiter = d ;
}
void InitAlleleInfo()
{
int i, j, k ;
alleleCnt = refSet.Size() ;
alleleInfo.ExpandTo(alleleCnt) ;
for ( i = 0 ; i < alleleCnt ; ++i )
{
char *allele = refSet.GetSeqName(i) ;
ParseAlleleName( allele, geneBuffer, majorAlleleBuffer ) ;
std::string sGene(geneBuffer) ;
std::string sMajorAllele(majorAlleleBuffer) ;
if (geneNameToIdx.find(sGene) == geneNameToIdx.end())
{
geneNameToIdx[sGene] = geneCnt ;
geneIdxToName.push_back(sGene) ;
++geneCnt ;
}
if (majorAlleleNameToIdx.find(sMajorAllele) == majorAlleleNameToIdx.end())
{
majorAlleleNameToIdx[sMajorAllele] = majorAlleleCnt ;
majorAlleleIdxToName.push_back(sMajorAllele) ;
majorAlleleSize.push_back(0);
++majorAlleleCnt ;
}
alleleInfo[i].abundance = 0 ;
alleleInfo[i].geneIdx = geneNameToIdx[sGene] ;
alleleInfo[i].majorAlleleIdx = majorAlleleNameToIdx[sMajorAllele] ;
alleleInfo[i].alleleRank = -1 ;
alleleInfo[i].abundance = 0 ;
alleleInfo[i].genotypeQuality = -1 ;
alleleInfo[i].whitelist = true ;
majorAlleleSize[ alleleInfo[i].majorAlleleIdx ] += refSet.GetSeqWeight(i) ;
}
// Compute gene similarity
geneSimilarity = new double*[geneCnt] ;
KmerCount *kmerProfiles = new KmerCount[geneCnt];
for (i = 0 ; i < geneCnt ; ++i)
{
geneSimilarity[i] = new double[geneCnt] ;
// Select the lexcographically smallest
int minTag = -1 ;
for (j = 0 ; j < alleleCnt ; ++j)
{
if (alleleInfo[j].geneIdx != i)
continue ;
if (minTag == -1 || strcmp(refSet.GetSeqConsensus(j), refSet.GetSeqConsensus(minTag) ) < 0)
minTag = j ;
}
kmerProfiles[i].AddCount(refSet.GetSeqConsensus(minTag)) ;
}
/*for (i = 0 ; i < geneCnt ; ++i)
{
geneSimilarity[i][i] = 1.0 ;
for (j = i + 1 ; j < geneCnt ; ++j)
{
geneSimilarity[i][j] = geneSimilarity[j][i] = kmerProfiles[i].GetCountSimilarityJaccard( kmerProfiles[j] ) ;
printf("%s %s %lf\n", geneIdxToName[i].c_str(), geneIdxToName[j].c_str(), geneSimilarity[i][j]) ;
}
}*/
for (i = 0 ; i < geneCnt ; ++i)
{
for (j = 0 ; j < geneCnt ; ++j)
{
if (i == j)
{
geneSimilarity[i][i] = 1.0 ;
continue ;
}
geneSimilarity[i][j] = kmerProfiles[i].GetCountSimilarity( kmerProfiles[j] ) ;
//printf("%s %s %lf\n", geneIdxToName[i].c_str(), geneIdxToName[j].c_str(), geneSimilarity[i][j]) ;
}
}
delete[] kmerProfiles ;
// Adjust allele effective length
std::map< int, std::vector<int> > geneIdxToAlleleIdx ;
for (i = 0 ; i < geneCnt ; ++i)
geneIdxToAlleleIdx[i] = std::vector<int>() ;
for (i = 0 ; i < alleleCnt ; ++i)
geneIdxToAlleleIdx[ alleleInfo[i].geneIdx ].push_back(i) ;
for (i = 0 ; i < geneCnt ; ++i)
{
SimpleVector<int> lens ;
std::vector<int> &alleleIds = geneIdxToAlleleIdx[i] ;
int size = alleleIds.size() ;
lens.ExpandTo(size) ;
for (j = 0 ; j < size ; ++j)
lens[j] = refSet.GetSeqEffectiveLen( alleleIds[j] ) ;
std::sort(lens.BeginAddress(), lens.EndAddress()) ;
int lenMode = 0 ;
int max = 0 ;
for (j = 0 ; j < size ; )
{
for (k = j ; k < size ; ++k)
if (lens[k] != lens[j])
break ;
if (k - j > max)
{
max = k - j ;
lenMode = lens[j] ;
}
j = k ;
}
const int largeDeletion = 500 ;
for (j = 0 ; j < size ; ++j)
{
if (refSet.GetSeqEffectiveLen( alleleIds[j] ) < lenMode - largeDeletion)
{
refSet.SetSeqEffectiveLen(alleleIds[j], lenMode) ;
//printf("%s changed\n", majorAlleleIdxToName[ alleleInfo[ alleleIds[j] ].majorAlleleIdx].c_str()) ;
}
}
}
}
void SetAlleleWhitelist(FILE *fpAlleleWhitelist)
{
char alleleName[256] ;
int i ;
for (i = 0 ; i < alleleCnt ; ++i)
alleleInfo[i].whitelist = false ;
std::set<int> selectedMajorAlleles ;
while (fscanf(fpAlleleWhitelist, "%s", alleleName) != EOF)
{
ParseAlleleName(alleleName, geneBuffer, majorAlleleBuffer) ;
std::string sMajorAllele(majorAlleleBuffer) ;
if (majorAlleleNameToIdx.find( sMajorAllele ) != majorAlleleNameToIdx.end() )
selectedMajorAlleles.insert( majorAlleleNameToIdx[sMajorAllele] ) ;
}
for (i = 0 ; i < alleleCnt ; ++i)
{
if (selectedMajorAlleles.find( alleleInfo[i].majorAlleleIdx ) != selectedMajorAlleles.end() )
alleleInfo[i].whitelist = true ;
}
}
void InitRefSet(char *filename)
{
int i, j ;
//refSet.InputRefFa(filename) ;
std::map<std::string, int> usedSeq ;
ReadFiles fa ;
fa.AddReadFile( filename, false ) ;
while ( fa.Next() )
{
std::string seq( fa.seq );
if (usedSeq.find(seq) != usedSeq.end())
{
refSet.UpdateSeqWeight( usedSeq[seq], 1) ;
}
else
{
usedSeq[seq] = refSet.InputRefSeq(fa.id, fa.seq, 1, true, fa.comment);
}
}
refSet.UpdateDnaSeqWeight() ;
InitAlleleInfo() ;
}
void InitRefSet(char *filename, const std::map<std::string, int> &selectedAlleles)
{
int i, j ;
//refSet.InputRefFa(filename) ;
std::map<std::string, int> usedSeq ;
ReadFiles fa ;
fa.AddReadFile( filename, false ) ;
while ( fa.Next() )
{
if (selectedAlleles.find(fa.id) == selectedAlleles.end())
continue ;
std::string seq( fa.seq );
if (usedSeq.find(seq) != usedSeq.end())
{
refSet.UpdateSeqWeight( usedSeq[seq], 1) ;
}
else
{
usedSeq[seq] = refSet.InputRefSeq(fa.id, fa.seq, 1, true, fa.comment);
}
}
refSet.UpdateDnaSeqWeight() ;
InitAlleleInfo() ;
}
void InitReadAssignments(int totalReadCnt, int maxAssignCnt)
{
maxAssignCnt = maxAssignCnt ;
readCnt = 0 ;
allReadAssignments.resize(totalReadCnt) ;
readAssignments.clear() ;
readAssignmentsFingerprintToIdx.clear() ;
readsInAllele.resize(alleleCnt) ;
int i ;
for (i = 0 ; i < totalReadCnt ; ++i)
allReadAssignments[i].clear() ;
for (i = 0 ; i < alleleCnt ; ++i)
readsInAllele[i].clear() ;
this->totalReadCnt = totalReadCnt ;
}
void SetReadAssignments(int readId, const std::vector<struct _fragmentOverlap> &assignment)
{
int i ;
int assignmentCnt = assignment.size() ;
allReadAssignments[readId].clear() ;
if (maxAssignCnt > 0 && assignmentCnt > maxAssignCnt)
return ;
/*for (i = 1 ; i < assignmentCnt ; ++i)
if ( alleleInfo[assignment[i].seqIdx].geneIdx != alleleInfo[assignment[i - 1].seqIdx].geneIdx)
return ;*/
double weightFactor = 1.0 ;
/*for (i = 1 ; i < assignmentCnt ; ++i)
if (assignment[i].overlap1.matchCnt != assignment[i - 1].overlap1.matchCnt ||
assignment[i].overlap2.matchCnt != assignment[i - 1].overlap2.matchCnt)
{
weightFactor = 0.1 ;
return;
break ;
}*/
for (i = 0 ; i < assignmentCnt ; ++i)
{
if (refSet.IsFragmentSpanSeparator(assignment[i]))
return;
}
double adjustFactor = 1.0 ;
double maxSimilarity = 0 ;
for (i = 0 ; i < assignmentCnt ; ++i)
{
if (assignment[i].similarity > maxSimilarity)
maxSimilarity = assignment[i].similarity ;
/*if (assignment[i].matchCnt != assignment[i - 1].matchCnt)
{
adjustFactor = 0 ;
break ;
}*/
}
if (maxSimilarity < 1)
adjustFactor = 0.25 ;
for (i = 0; i < assignmentCnt; ++i)
{
struct _readAssignment na ;
if (!alleleInfo[ assignment[i].seqIdx ].whitelist)
continue ;
na.alleleIdx = assignment[i].seqIdx ;
na.start = assignment[i].seqStart ;
na.end = assignment[i].seqEnd ;
na.weight = ReadAssignmentWeight(assignment[i]);
na.qual = assignment[i].qual ;
na.adjustWeight = adjustFactor * na.weight ;
allReadAssignments[readId].push_back(na) ;
}
}
// Should only be called on the reads without coalescing
std::vector<struct _readAssignment> GetReadAssignments(int readId)
{
return allReadAssignments[readId] ;
}
// Coalesce the [begin,end] all reads to the read assignment
int CoalesceReadAssignments(int begin, int end)
{
int i, j, k ;
int ret = 0 ;
for (i = begin ; i <= end && i < totalReadCnt ; ++i)
{
const int FINGERPRINT_MAX = 20000003 ;
int size = allReadAssignments[i].size() ;
if (size == 0)
continue ;
++ret ;
std::sort(allReadAssignments[i].begin(), allReadAssignments[i].end()) ;
int fingerprint = 0 ;
for (j = 0 ; j < size ; ++j)
{
k = allReadAssignments[i][j].alleleIdx ;
fingerprint = (fingerprint * (int64_t)alleleCnt + k) % FINGERPRINT_MAX ;
}
int addTo = -1 ;
if (readAssignmentsFingerprintToIdx.find(fingerprint) == readAssignmentsFingerprintToIdx.end())
addTo = -1 ;
else
{
std::vector<int> assignmentsIdx = readAssignmentsFingerprintToIdx[fingerprint] ;
int idxSize = assignmentsIdx.size() ;
addTo = -1 ;
for (j = 0 ; j < idxSize ; ++j)
{
if (IsReadAssignmentTheSame(allReadAssignments[i],
readAssignments[ assignmentsIdx[j] ] ) )
{
addTo = assignmentsIdx[j] ;
break ;
}
}
}
if (addTo == -1)
{
readAssignments.push_back( allReadAssignments[i] ) ;
readAssignmentsFingerprintToIdx[fingerprint].push_back(readCnt) ;
++readCnt ;
}
else
{
for (j = 0 ; j < size ; ++j)
{
if (allReadAssignments[i][j].qual == 1)
{
if (allReadAssignments[i][j].start < readAssignments[addTo][j].start)
readAssignments[addTo][j].start = allReadAssignments[i][j].start ;
if (allReadAssignments[i][j].end < readAssignments[addTo][j].end)
readAssignments[addTo][j].end = allReadAssignments[i][j].start ;
}
readAssignments[addTo][j].weight += allReadAssignments[i][j].weight ;
readAssignments[addTo][j].adjustWeight += allReadAssignments[i][j].adjustWeight ;
// The read assignment the same test makes sure they have the same quality
}
}
}
// Release the memory space for allReadAssignments
for (i = begin ; i <= end && i < totalReadCnt ; ++i)
{
std::vector<struct _readAssignment>().swap(allReadAssignments[i]) ;
}
return ret ;
}
// Build the read in allele list
// Ret: the number of read got assigned
int FinalizeReadAssignments()
{
int i, j ;
int ret = 0 ;
for (i = 0 ; i < readCnt ; ++i)
{
int assignmentCnt = readAssignments[i].size() ;
//std::sort(readAssignments[i].begin(), readAssignments[i].end()) ;
if (assignmentCnt > 0)
++ret ;
for (j = 0; j < assignmentCnt; ++j)
{
struct _pair np ;
np.a = i ;
np.b = j ;
readsInAllele[readAssignments[i][j].alleleIdx].push_back(np) ;
}
}
BuildAlleleEquivalentClass() ;
for (i = 0 ; i < alleleCnt ; ++i)
{
alleleInfo[i].missingCoverage = refSet.GetSeqMissingBaseCoverage(i, 0.01) ;
//printf("%d %s %d\n", i, refSet.GetSeqName(i), alleleMissingCoverage[i]) ;
}
return ret ;
}
double GetAverageReadAssignmentCnt()
{
int i ;
double sum = 0 ;
double cnt = 0 ;
for (i = 0 ; i < readCnt ; ++i)
{
if (readAssignments[i].size() > 0)
{
sum += readAssignments[i].size() ;
++cnt ;
}
}
return sum / cnt ;
}
void SetAlleleAbundance(double *ecReadCount, struct _ecInfo *ecInfo)
{
int i, j, k ;
int ecCnt = equivalentClassToAlleles.size();
if (ecReadCount != NULL)
{
for (i = 0 ; i < alleleCnt ; ++i)
alleleInfo[i].abundance = alleleInfo[i].ecAbundance = 0 ;
for (i = 0 ; i < ecCnt ; ++i)
{
int size = equivalentClassToAlleles[i].size() ;
double abund = 0 ;
//k = equivalentClassToAlleles[i][0] ;
//printf("%d %d %s %lf %d %d\n", i, k, refSet.GetSeqName(k), emEcReadCount[j][i], refSet.GetSeqConsensusLen(k),readsInAllele[k].size()) ;
abund += ecReadCount[i] ;
//printf("%lf\n", abund) ;
abund = abund / ecInfo[i].length * 1000.0 ; // FPK
for (j = 0 ; j < size ; ++j)
{
k = equivalentClassToAlleles[i][j] ;
//alleleInfo[k].abundance = ecAbundance[i] / size * effectiveReadCnt ;
//alleleInfo[k].ecAbundance = ecAbundance[i] * effectiveReadCnt ;
alleleInfo[k].abundance = abund / size ;
alleleInfo[k].ecAbundance = abund ;
//printf("%d %d %s %lf %d %d\n", i, k, refSet.GetSeqName(k), (double)abund, refSet.GetSeqConsensusLen(k),readsInAllele[k].size()) ;
}
//printf("%lf %lf\n", ecAbundance[i], ecAbundance[i] * effectiveReadCnt) ;
}
}
// Set major allele and gene abundances
// Init other useful abundance data
geneAbundance.ExpandTo(geneCnt) ;
geneAbundance.SetZero(0, geneCnt) ;
majorAlleleAbundance.ExpandTo(majorAlleleCnt) ;
majorAlleleAbundance.SetZero(0, majorAlleleCnt) ;
geneMaxMajorAlleleAbundance.ExpandTo(geneCnt) ;
geneMaxMajorAlleleAbundance.SetZero(0, geneCnt) ;
for (i = 0 ; i < alleleCnt ; ++i)
{
majorAlleleAbundance[ alleleInfo[i].majorAlleleIdx ] += alleleInfo[i].abundance ;
geneAbundance[ alleleInfo[i].geneIdx ] += alleleInfo[i].abundance ;
/*if (alleleInfo[i].abundance > geneMaxMajorAlleleAbundance[ alleleInfo[i].geneIdx ])
{
geneMaxMajorAlleleAbundance[ alleleInfo[i].geneIdx ] = alleleInfo[i].abundance ;
}*/
}
for (i = 0 ; i < alleleCnt ; ++i)
{