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SubexonInfo.cpp
1214 lines (1082 loc) · 35.3 KB
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SubexonInfo.cpp
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// report the total depth for the segment
// Format: ./a.out alignments.bam splice_site
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#define __STDC_FORMAT_MACROS
#include <inttypes.h>
#include <algorithm>
#include <vector>
#include <math.h>
#include "alignments.hpp"
#include "blocks.hpp"
#include "stats.hpp"
#define ABS(x) ((x)<0?-(x):(x))
char usage[] = "./subexon-info alignment.bam intron.splice [options]\n"
"options:\n"
"\t--minDepth INT: the minimum coverage depth considered as part of a subexon (default: 2)\n"
"\t--noStats: do not compute the statistical scores (default: not used)\n" ;
char buffer[4096] ;
int gMinDepth ;
bool CompSplitSite( struct _splitSite a, struct _splitSite b )
{
if ( a.chrId < b.chrId )
return true ;
else if ( a.chrId > b.chrId )
return false ;
else if ( a.pos != b.pos )
return a.pos < b.pos ;
else if ( a.type != b.type )
return a.type < b.type ; // We want the start of exons comes first, since we are scanning the genome from left to right,
// so we can terminate the extension early and create a single-base subexon later.
else
return a.oppositePos < b.oppositePos ;
}
bool CompBlocksByAvgDepth( struct _block a, struct _block b )
{
double avgA = a.depthSum / (double)( a.end - a.start + 1 ) ;
double avgB = b.depthSum / (double)( b.end - b.start + 1 ) ;
return avgA < avgB ;
}
bool CompBlocksByRatio( struct _block a, struct _block b )
{
return a.ratio < b.ratio ;
}
int CompDouble( const void *p1, const void *p2 )
{
double a = *(double *)p1 ;
double b = *(double *)p2 ;
if ( a > b )
return 1 ;
else if ( a < b )
return -1 ;
else
return 0 ;
}
// Clean up the split sites;
void FilterAndSortSplitSites( std::vector<struct _splitSite> &sites )
{
std::sort( sites.begin(), sites.end(), CompSplitSite ) ;
int i, j, k, l ;
int size = sites.size() ;
for ( i = 0 ; i < size ; )
{
for ( j = i + 1 ; j < size ; ++j )
if ( sites[j].chrId != sites[i].chrId || sites[j].pos != sites[i].pos || sites[j].type != sites[i].type )
break ;
int maxSupport = 0 ;
for ( k = i ; k < j ; ++k )
if ( sites[k].support > maxSupport )
maxSupport = sites[k].support ;
int strandCnt[2] = {0, 0} ;
char strand = sites[i].strand ;
for ( k = i ; k < j ; ++k )
{
if ( sites[k].strand == '-' )
strandCnt[0] += sites[k].support ;
else if ( sites[k].strand == '+' )
strandCnt[1] += sites[k].support ;
}
if ( strandCnt[0] > strandCnt[1] )
strand = '-' ;
else if ( strandCnt[1] > strandCnt[0] )
strand = '+' ;
bool allOneExceptMax = false ;
if ( maxSupport >= 20 )
{
allOneExceptMax = true ;
for ( k = i ; k < j ; ++k )
{
if ( sites[k].support == maxSupport )
continue ;
if ( sites[k].support > 1 )
{
allOneExceptMax = false ;
break ;
}
}
}
for ( k = i ; k < j ; ++k )
{
if ( ( sites[k].support < 0.01 * maxSupport && sites[k].support <= 3 )
|| sites[k].support < 0.001 * maxSupport || sites[k].strand != strand // The introns from the different strand are filtered.
|| ( sites[k].support < 0.02 * maxSupport && sites[k].mismatchSum >= 2 * sites[k].support )
|| ( allOneExceptMax && sites[k].support == 1 )
|| ( sites[k].support <= 2 && sites[k].mismatchSum >= 2 * sites[k].support )
|| ( maxSupport >= 2 && sites[k].support == 1 && ( ABS( sites[k].oppositePos - sites[k].pos - 1 ) >= 10000 || sites[k].mismatchSum != 0 ) ) )
{
for ( l = i - 1 ; l >= 0 && sites[l].chrId == sites[i].chrId && sites[l].pos >= sites[k].oppositePos ; --l )
{
if ( sites[l].pos == sites[k].oppositePos && sites[l].oppositePos == sites[k].pos )
{
sites[l].support = -1 ;
sites[k].support = -1 ;
break ;
}
}
for ( l = j ; l < size && sites[l].chrId == sites[i].chrId && sites[l].pos <= sites[k].oppositePos ; ++l )
{
if ( sites[l].pos == sites[k].oppositePos && sites[l].oppositePos == sites[k].pos )
{
sites[l].support = -1 ;
sites[k].support = -1 ;
break ;
}
}
}
/*else if ( sites[k].support <= 1 && sites[k].oppositePos - sites[k].pos + 1 >= 30000 )
{
for ( l = j ; l < size && sites[l].chrId == sites[i].chrId && sites[l].pos <= sites[k].oppositePos ; ++l )
{
if ( sites[l].pos == sites[k].oppositePos && sites[l].oppositePos == sites[k].pos )
{
if ( l - j >= 20 )
{
sites[l].support = -1 ;
sites[k].support = -1 ;
break ;
}
}
}
}*/
}
i = j ;
}
k = 0 ;
for ( i = 0 ; i < size ; ++i )
{
if ( sites[i].support > 0 )
{
sites[k] = sites[i] ;
if ( sites[k].strand == '?' )
sites[k].strand = '.' ;
++k ;
}
}
sites.resize( k ) ;
}
// Remove the same sites.
void KeepUniqSplitSites( std::vector< struct _splitSite> &sites )
{
int i, j ;
int size = sites.size() ;
int k = 0 ;
for ( i = 0 ; i < size ; )
{
for ( j = i + 1 ; j < size ; ++j )
if ( sites[j].chrId != sites[i].chrId || sites[j].pos != sites[i].pos || sites[j].type != sites[i].type )
break ;
sites[k] = sites[i] ;
++k ;
/*else
{
if ( sites[i].type != sites[k-1].type )
{
printf( "%d\n", sites[i].pos ) ;
}
}*/
i = j ;
}
sites.resize( k ) ;
// For the sites that corresponds to the start of an exon, we remove the adjust to it and
/*for ( i = 1 ; i < size ; ++i )
{
if ( sites[i].pos == sites[i - 1].pos && sites[i - 1].type == 2 )
{
if ( sites[i].type == 1 )
++sites[i].pos ;
}
}*/
}
// Filter split sites that are extremely close to each other but on different strand.
void FilterNearSplitSites( std::vector< struct _splitSite> &sites )
{
int i, j ;
int size = sites.size() ;
int k = 0 ;
for ( i = 0 ; i < size - 1 ; ++i )
{
if ( sites[i].support < 0 || sites[i].type != sites[i + 1].type || sites[i].chrId != sites[i + 1].chrId )
continue ;
if ( sites[i + 1].pos - sites[i].pos <= 7 &&
( sites[i + 1].strand != sites[i].strand ||
sites[i].strand == '?' ) )
{
int tag = i ;
if ( sites[i + 1].support < sites[i].support )
tag = i + 1 ;
sites[tag].support = -1 ;
int direction ;
if ( sites[tag].oppositePos < sites[tag].pos )
direction = -1 ;
else
direction = 1 ;
for ( j = tag ; j >= 0 && j < size ; j += direction )
if ( sites[j].pos == sites[tag].oppositePos && sites[j].oppositePos == sites[tag].pos )
{
sites[j].support = -1 ;
break ;
}
}
}
for ( i = 0 ; i < size ; ++i )
{
if ( sites[i].support > 0 )
{
sites[k] = sites[i] ;
++k ;
}
}
sites.resize( k ) ;
}
void FilterRepeatSplitSites( std::vector<struct _splitSite> &sites )
{
int i, j ;
int size = sites.size() ;
int k = 0 ;
for ( i = 0 ; i < size ; )
{
for ( j = i + 1 ; j < size ; ++j )
{
if ( sites[j].pos != sites[i].pos || sites[j].type != sites[i].type || sites[i].chrId != sites[j].chrId )
break ;
}
int max = -1 ;
int maxtag = 0 ;
for ( k = i ; k < j ; ++k )
{
if ( sites[k].uniqSupport > max )
{
max = sites[k].uniqSupport ;
maxtag = k ;
}
else if ( sites[k].uniqSupport == max && sites[k].support > sites[maxtag].support )
{
maxtag = k ;
}
}
if ( max > -1 )
{
if ( sites[maxtag].uniqSupport > sites[maxtag].support * 0.1 )
{
for ( k = i ; k < j ; ++k )
if ( sites[k].uniqSupport < 0.05 * sites[k].support )
{
sites[k].support = -1 ;
int direction ;
if ( sites[k].oppositePos < sites[k].pos )
direction = -1 ;
else
direction = 1 ;
int l ;
for ( l = k ; l >= 0 && l < size ; l += direction )
if ( sites[l].pos == sites[k].oppositePos && sites[l].oppositePos == sites[k].pos )
{
sites[l].support = -1 ;
break ;
}
}
}
else
{
for ( k = i ; k < j ; ++k )
{
if ( sites[k].support <= 10 )
{
sites[k].support = -1 ;
int direction ;
if ( sites[k].oppositePos < sites[k].pos )
direction = -1 ;
else
direction = 1 ;
int l ;
for ( l = k ; l >= 0 && l < size ; l += direction )
if ( sites[l].pos == sites[k].oppositePos && sites[l].oppositePos == sites[k].pos )
{
sites[l].support = -1 ;
break ;
}
}
}
}
}
i = j ;
}
k = 0 ;
for ( i = 0 ; i < size ; ++i )
{
if ( sites[i].support > 0 )
{
sites[k] = sites[i] ;
++k ;
}
}
sites.resize( k ) ;
}
// When k=1, the gamma distribution becomes exponential distribution, and can be optimized analytically..
// Maximize: sum_i z_i( log( 1/theta e^{-x_i / theta} )
/*double ThetaOfExponentialDistribution( double *x, double *z, int n )
{
double sumZ = 0;
double sumZX = 0 ;
for ( i = 0 ; i < n ; ++i )
{
sumZ += z[i] ;
sumZX += z[i] * x[i] ;
}
}*/
// for boundK, if it is positive, it represent the upper bound. If it is negative, -boundK will be the lower bound for k.
// if boundK==0, there is no extra bound.
// The same logic for boundProduct, which bounds k*theta
void GradientDescentGammaDistribution( double &k, double &theta, double initK, double initTheta, double lowerBoundK, double upperBoundK,
double lowerBoundMean, double upperBoundMean, double *x, double *z, int n )
{
int i ;
k = initK ;
theta = initTheta ;
double c = 0.5 ;
int iterCnt = 1 ;
double sumZ = 0 ;
double sumZX = 0 ;
double sumZLogX = 0 ;
double Hessian[2][2] ; // 0 for k, 1 for theta
double inverseHessian[2][2] ;
int tmp ;
double positiveKRecord = -5, positiveThetaRecord = -5 ; // record the value of k, theta when theta, k becomes non-positive.
for ( i = 0 ; i < n ; ++i )
{
sumZ += z[i] ;
sumZX += z[i] * x[i] ;
sumZLogX += z[i] * log( x[i] ) ;
}
while ( 1 )
{
double gradK = 0 ;
double gradTheta = 0 ;
double prevK = k ;
double prevTheta = theta ;
double digammaK = digammal( k ) ;
gradK = sumZ * ( -log( theta ) - digammaK ) + sumZLogX ;
gradTheta = -sumZ * ( k / theta ) + sumZX / ( theta * theta ) ;
Hessian[0][0] = -sumZ * trigamma( k, &tmp ) ;
Hessian[0][1] = -sumZ / theta ; // \partial l / ( \partial k \partial theta)
Hessian[1][0] = -sumZ / theta ;
Hessian[1][1] = sumZ * k / ( theta * theta ) - 2 * sumZX / ( theta * theta * theta ) ;
double det = Hessian[0][0] * Hessian[1][1] - Hessian[0][1] * Hessian[1][0] ;
/*printf( "%s iter %d:\n", __func__, iterCnt ) ;
printf( "%lf %lf %lf %lf\n", sumZ, k, theta, sumZX ) ;
printf( "%lf %lf %lf\n", gradK, gradTheta, det ) ;
printf( "%lf %lf %lf %lf\n", Hessian[0][0], Hessian[0][1], Hessian[1][0], Hessian[1][1] ) ;*/
if ( det <= 1e-4 && det >=-1e-4 )
{
k = k + c / iterCnt * gradK ;
theta = theta + c / iterCnt * gradTheta ;
}
else
{
inverseHessian[0][0] = Hessian[1][1] / det ;
inverseHessian[0][1] = -Hessian[0][1] / det ;
inverseHessian[1][0] = -Hessian[1][0] / det ;
inverseHessian[1][1] = Hessian[0][0] / det ;
//printf( "%lf %lf %lf %lf: %lf\n=====\n", inverseHessian[0][0], inverseHessian[0][1], inverseHessian[1][0], inverseHessian[1][1],
// Hessian[1][0] * inverseHessian[0][1] + Hessian[1][1] * inverseHessian[1][1] ) ;
double step = 0.5 ;
k = k - step * ( inverseHessian[0][0] * gradK + inverseHessian[0][1] * gradTheta ) ;
theta = theta - step * ( inverseHessian[1][0] * gradK + inverseHessian[1][1] * gradTheta ) ;
bool flag = false ;
if ( k <= 1e-6 )
{
step = ( prevK - 1e-6 ) / ( inverseHessian[0][0] * gradK + inverseHessian[0][1] * gradTheta ) ;
if ( ABS( theta - positiveThetaRecord ) < 1e-5 )
flag = true ;
positiveThetaRecord = theta ;
}
if ( theta <= 1e-6 )
{
double tmp = ( prevTheta - 1e-6 ) / ( inverseHessian[1][0] * gradK + inverseHessian[1][1] * gradTheta ) ;
if ( tmp < step )
step = tmp ;
if ( ABS( k - positiveKRecord ) < 1e-5 )
flag = true ;
positiveKRecord = k ;
}
if ( step != 0.5 )
{
k = prevK - step * ( inverseHessian[0][0] * gradK + inverseHessian[0][1] * gradTheta ) ;
theta = prevTheta - step * ( inverseHessian[1][0] * gradK + inverseHessian[1][1] * gradTheta ) ;
}
/*if ( flag )
{
k = prevK ;
theta = prevTheta ;
break ;
}*/
}
if ( upperBoundK > 0 && k > upperBoundK )
k = upperBoundK ;
else if ( lowerBoundK > 0 && k < lowerBoundK )
k = lowerBoundK ;
if ( upperBoundMean > 0 && k * theta > upperBoundMean )
{
theta = upperBoundMean / k ;
}
else if ( lowerBoundMean > 0 && k * theta < lowerBoundMean )
{
theta = lowerBoundMean / k ;
}
if ( k <= 1e-6 )
{
k = 1e-6 ;
}
if ( theta <= 1e-6 )
{
theta = 1e-6 ;
}
double diff = ABS( prevK - k ) + ABS( prevTheta - theta ) ;
if ( diff < 1e-5 )
break ;
++iterCnt ;
//if ( det <= 1e-4 && det >=-1e-4 && k >= 5000 ) //&& diff < 1 )
if ( k >= 10000 ) //&& diff < 1 )
{
k = prevK ;
theta = prevTheta ;
break ;
}
if ( iterCnt == 1000 )
break ;
}
}
double MixtureGammaEM( double *x, int n, double &pi, double *k, double *theta, int tries, double meanBound[2], int iter = 1000 )
{
int i ;
if ( n <= 0 )
return 0 ;
double *z = new double[n] ; // the expectation that it assigned to model 0.
double *oneMinusZ = new double[n] ;
int t = 0 ;
double history[5] = {-1, -1, -1, -1, -1} ;
double maxX = -1 ;
double sumX = 0 ;
for ( i = 0 ; i < n ; ++i )
{
sumX += x[i] ;
if ( x[i] > maxX )
maxX = x[i] ;
}
if ( maxX > meanBound[1] && meanBound[1] >= 0 )
maxX = meanBound[1] ;
/*if ( meanBound[1] == -1 )
{
// The EM for coverage
maxX = 10.0 ;
}*/
while ( 1 )
{
double npi, nk[2], ntheta[2] ;
double sum = 0 ;
for ( i = 0 ; i < n ; ++i )
{
//double lf0 = -k[0] * log( theta[0] ) + ( k[0] - 1 ) * log( cov[i]) - cov[i] / theta[0] - lgamma( k[0] );
//double lf1 = -k[1] * log( theta[1] ) + ( k[1] - 1 ) * log( cov[i]) - cov[i] / theta[1] - lgamma( k[1] );
//z[i] = exp( lf0 + log( pi ) ) / ( exp( lf0 + log( pi ) ) + exp( lf1 + log( 1 - pi ) ) ) ;
if ( pi != 0 )
z[i] = MixtureGammaAssignment( x[i], pi, k, theta ) ;
else
z[i] = 0 ;
/*if ( isnan( z[i] ) )
{
printf( "nan: %lf %lf %lf %lf\n", x[i], pi, k, theta ) ;
}*/
oneMinusZ[i] = 1 - z[i] ;
sum += z[i] ;
}
// compute new pi.
npi = sum / n ;
// Use gradient descent to compute new k and theta.
if ( 1 ) //pi > 0 )
{
double bound ;
if ( meanBound[1] != -1 ) // the EM for ratio
{
bound = ( theta[1] * k[1] > 1 ) ? 1 : ( theta[1] * k[1] ) / ( 1 + tries );
GradientDescentGammaDistribution( nk[0], ntheta[0], k[0], theta[0], k[1], -1, -1, bound, x, z, n ) ; // It seems setting an upper bound 1 for k[0] is not a good idea.
}
else
{
bound = ( theta[1] * k[1] > 1 ) ? 1 : ( theta[1] * k[1] ) / ( 1 + tries ) ;
GradientDescentGammaDistribution( nk[0], ntheta[0], k[0], theta[0], k[1], -1, meanBound[0], bound, x, z, n ) ; // It seems setting an upper bound 1 for k[0] is not a good idea.
}
GradientDescentGammaDistribution( nk[1], ntheta[1], k[1], theta[1], -1, k[0], theta[0] * k[0], maxX, x, oneMinusZ, n ) ;
}
else
{
GradientDescentGammaDistribution( nk[1], ntheta[1], k[1], theta[1], 0, 0, 0, 0, x, oneMinusZ, n ) ;
}
double diff ;
if ( isnan( npi ) || isnan( nk[0] ) || isnan( nk[1] ) || isnan( ntheta[0] ) || isnan( ntheta[1] ) )
{
delete[] z ;
delete[] oneMinusZ ;
return -1 ;
}
diff = ABS( nk[0] - k[0] ) + ABS( nk[1] - k[1] )
+ ABS( ntheta[0] - theta[0] ) + ABS( ntheta[1] - theta[1] ) ; // pi is fully determined by these 4 parameters.
if ( diff < 1e-4 )
break ;
diff = ABS( nk[0] - history[1] ) + ABS( nk[1] - history[2] )
+ ABS( ntheta[0] - history[3] ) + ABS( ntheta[1] - history[4] ) ; // pi is fully determined by these 4 parameters.
if ( diff < 1e-4 )
break ;
history[0] = pi ;
history[1] = k[0] ;
history[2] = k[1] ;
history[3] = theta[0] ;
history[4] = theta[1] ;
pi = npi ;
k[0] = nk[0] ;
k[1] = nk[1] ;
theta[0] = ntheta[0] ;
theta[1] = ntheta[1] ;
/*double logLikelihood = 0 ;
for ( i = 0 ; i < n ; ++i )
logLikelihood += log( pi * exp( LogGammaDensity( x[i], k[0], theta[0]) ) +
(1 - pi ) * exp( LogGammaDensity( x[i], k[1], theta[1] ) ) ) ;*/
//printf( "%d: %lf %lf %lf %lf %lf\n", t, pi, k[0], theta[0], k[1], theta[1] ) ;
++t ;
if ( iter != -1 && t >= iter )
break ;
}
delete[] z ;
delete[] oneMinusZ ;
return 0 ;
}
bool IsParametersTheSame( double *k, double *theta )
{
if ( ABS( k[0] - k[1] ) < 1e-2 && ABS( theta[0] - theta[1] ) < 1e-2 )
return true ;
return false ;
}
int RatioAndCovEM( double *covRatio, double *cov, int n, double &piRatio, double kRatio[2],
double thetaRatio[2], double &piCov, double kCov[2], double thetaCov[2] )
{
int i ;
piRatio = 0.6 ; // mixture coefficient for model 0 and 1
kRatio[0] = 0.9 ;
kRatio[1] = 0.45 ;
thetaRatio[0] = 0.05 ;
thetaRatio[1] = 1 ;
double meanBound[2] = {-1, 1} ; // [0] is for the lower bound of the noise model, [1] is for the upper bound of the true model
/*double *filteredCovRatio = new double[n] ;// ignore the ratio that is greater than 5.
int m = 0 ;
for ( i = 0 ; i < n ; ++i )
if ( covRatio[i] < 1.0 )
{
filteredCovRatio[m] = covRatio[i] ;
++m ;
}*/
srand( 17 ) ;
int maxTries = 10 ;
int t = 0 ;
double *buffer = new double[n] ;
for ( i = 0 ; i < n ; ++i )
buffer[i] = covRatio[i] ;
qsort( buffer, n, sizeof( double ), CompDouble ) ;
//covRatio = buffer ;
while ( 1 )
{
//printf( "EM\n" ) ;
MixtureGammaEM( covRatio, n, piRatio, kRatio, thetaRatio, t, meanBound ) ;
//printf( "%lf %lf %lf %lf %lf\n", piRatio, kRatio[0], kRatio[1], thetaRatio[0], thetaRatio[1] ) ;
if ( piRatio > 0.999 || piRatio < 0.001 || IsParametersTheSame( kRatio, thetaRatio ) )
{
++t ;
if ( t > maxTries )
break ;
piRatio = 0.6 ;
kRatio[0] += ( ( rand() * 0.5 - RAND_MAX ) / (double)RAND_MAX * 0.1 ) ;
if ( kRatio[0] <= 0 )
kRatio[0] = 0.9 ;
kRatio[1] += ( ( rand() * 0.5 - RAND_MAX ) / (double)RAND_MAX * 0.1 ) ;
if ( kRatio[1] <= 0 )
kRatio[1] = 0.45 ;
thetaRatio[0] += ( ( rand() * 0.5 - RAND_MAX ) / (double)RAND_MAX * 0.1 ) ;
if ( thetaRatio[0] <= 0 )
thetaRatio[0] = 0.05 ;
thetaRatio[1] += ( ( rand() * 0.5 - RAND_MAX ) / (double)RAND_MAX * 0.1 ) ;
if ( thetaRatio[1] <= 0 )
thetaRatio[1] = 1 ;
if ( kRatio[0] < kRatio[1] )
{
if ( rand() & 1 )
kRatio[0] = kRatio[1] ;
else
kRatio[1] = kRatio[0] ;
}
if ( kRatio[0] * thetaRatio[0] > kRatio[1] * thetaRatio[1] )
{
thetaRatio[0] = kRatio[1] * thetaRatio[1] / kRatio[0] ;
}
//printf( "%lf %lf %lf %lf %lf\n", piRatio, kRatio[0], kRatio[1], thetaRatio[0], thetaRatio[1] ) ;
continue ;
}
break ;
}
//delete[] filteredCovRatio ;
if ( t > maxTries && piRatio > 0.999 )
{
/*piRatio = 0.6 ; // mixture coefficient for model 0 and 1
kRatio[0] = 0.9 ;
kRatio[1] = 0.45 ;
thetaRatio[0] = 0.05 ;
thetaRatio[1] = 1 ;*/
piRatio = 0.999 ;
}
if ( IsParametersTheSame( kRatio, thetaRatio ) || piRatio <= 1e-3 )
piRatio = 1e-3 ;
piCov = piRatio ; // mixture coefficient for model 0 and 1
kCov[0] = 0.9 ;
kCov[1] = 0.45 ;
thetaCov[0] = 3 ;
thetaCov[1] = 12 ;
// only do one iteration of EM, so that pi does not change?
// But it seems it still better to run full EM.
meanBound[0] = 1.01 ;
meanBound[1] = -1 ;
//printf( "for coverage:\n" ) ;
//piCov = 0.001000 ;
MixtureGammaEM( cov, n, piCov, kCov, thetaCov, 0, meanBound ) ;
//printf( "for coverage done\n" ) ;
piCov = piRatio ;
delete []buffer ;
return 0 ;
}
double GetPValue( double x, double *k, double *theta )
{
int fault ;
double p ;
p = 1 - gammad( x / theta[0], k[0], &fault ) ;
return p ;
}
// if x's value is less than the average of (k0-1)*theta0, then we force x=(k0-1)*theta0,
// the mode of the model 0. Of course, it does not affect when k0<=1 already.
double MixtureGammaAssignmentAdjust( double x, double pi, double* k, double *theta )
{
if ( x < ( k[0] - 1 ) * theta[0] )
{
x = ( k[0] - 1 ) * theta[0] ;
}
return MixtureGammaAssignment( x, pi, k, theta ) ;
}
// Transform the cov number for better fitting
double TransformCov( double c )
{
double ret ;
// original it is c-1.
// Use -2 instead of -1 is that many region covered to 1 reads will be filtered when
// build the subexons.
//
//ret = sqrt( c ) - 1 ;
if ( c <= 2 + 1e-6 )
ret = 1e-6 ;
else
ret = c - 2 ;
return ret ;
//return log( c ) / log( 2.0 ) ;
}
int main( int argc, char *argv[] )
{
int i, j ;
bool noStats = false ;
if ( argc < 3 )
{
fprintf( stderr, usage ) ;
exit( 1 ) ;
}
gMinDepth = 2 ;
for ( i = 3 ; i < argc ; ++i )
{
if ( !strcmp( argv[i], "--noStats" ) )
{
noStats = true ;
continue ;
}
else if ( !strcmp( argv[i], "--minDepth" ) )
{
gMinDepth = atoi( argv[i + 1] ) ;
++i ;
continue ;
}
else
{
fprintf( stderr, "Unknown argument: %s\n", argv[i] ) ;
return 0 ;
}
}
Alignments alignments ;
alignments.Open( argv[1] ) ;
std::vector<struct _splitSite> splitSites ; // only compromised the
std::vector<struct _splitSite> allSplitSites ;
// read in the splice site
FILE *fp ;
fp = fopen( argv[2], "r" ) ;
char chrom[50] ;
int64_t start, end ;
int support ;
char strand[3] ;
int uniqSupport, secondarySupport, uniqEditDistance, secondaryEditDistance ;
while ( fscanf( fp, "%s %" PRId64 " %" PRId64 " %d %s %d %d %d %d", chrom, &start, &end, &support, strand,
&uniqSupport, &secondarySupport, &uniqEditDistance, &secondaryEditDistance ) != EOF )
{
if ( support <= 0 )
continue ;
//if ( !( uniqSupport >= 1
// || secondarySupport > 10 ) )
//if ( uniqSupport <= 0.01 * ( uniqSupport + secondarySupport ) || ( uniqSupport == 0 && secondarySupport < 20 ) )
//if ( uniqSupport == 0 && secondarySupport <= 10 )
// continue ;
int chrId = alignments.GetChromIdFromName( chrom ) ;
struct _splitSite ss ;
--start ;
--end ;
ss.pos = start ;
ss.chrId = chrId ;
ss.type = 2 ;
ss.oppositePos = end ;
ss.strand = strand[0] ;
ss.support = support ;
ss.uniqSupport = uniqSupport ;
ss.mismatchSum = uniqEditDistance + secondaryEditDistance ;
splitSites.push_back( ss ) ;
ss.pos = end ;
ss.type = 1 ;
ss.oppositePos = start ;
ss.strand = strand[0] ;
ss.support = support ;
ss.uniqSupport = uniqSupport ;
ss.mismatchSum = uniqEditDistance + secondaryEditDistance ;
splitSites.push_back( ss ) ;
}
fclose( fp ) ;
//printf( "ss:%d\n", splitSites.size() ) ;
//printf( "ss:%d\n", splitSites.size() ) ;
alignments.GetGeneralInfo( true ) ;
// Build the blocks
Blocks regions ;
alignments.Rewind() ;
regions.BuildExonBlocks( alignments ) ;
//printf( "%d\n", regions.exonBlocks.size() ) ;
FilterAndSortSplitSites( splitSites ) ;
FilterNearSplitSites( splitSites ) ;
FilterRepeatSplitSites( splitSites ) ;
regions.FilterSplitSitesInRegions( splitSites ) ;
regions.FilterGeneMergeSplitSites( splitSites ) ;
allSplitSites = splitSites ;
KeepUniqSplitSites( splitSites ) ;
//for ( i = 0 ; i < splitSites.size() ; ++i )
// printf( "%d %d\n", splitSites[i].pos + 1, splitSites[i].oppositePos + 1 ) ;
// Split the blocks using split site
regions.SplitBlocks( alignments, splitSites ) ;
//printf( "%d\n", regions.exonBlocks.size() ) ;
/*for ( i = 0 ; i < regions.exonBlocks.size() ; ++i )
{
struct _block &e = regions.exonBlocks[i] ;
printf( "%s %" PRId64 " %" PRId64 " %d %d\n", alignments.GetChromName( e.chrId ), e.start + 1, e.end + 1, e.leftType, e.rightType ) ;
}
return 0 ;*/
// Recompute the coverage for each block.
alignments.Rewind() ;
//printf( "Before computeDepth: %d\n", regions.exonBlocks.size() ) ;
regions.ComputeDepth( alignments ) ;
//printf( "After computeDepth: %d\n", regions.exonBlocks.size() ) ;
// Merge blocks that may have a hollow coverage by accident.
regions.MergeNearBlocks() ;
//printf( "After merge: %d\n", regions.exonBlocks.size() ) ;
// Put the intron informations
regions.AddIntronInformation( allSplitSites, alignments ) ;
//printf( "After add information.\n" ) ;
// Compute the average ratio against the left and right connected subexons.
regions.ComputeRatios() ;
//printf( "After compute ratios.\n" ) ;
//printf( "Finish building regions.\n" ) ;
if ( noStats )
{
// just output the subexons.
if ( realpath( argv[1], buffer ) == NULL )
{
strcpy( buffer, argv[1] ) ;
}
printf( "#%s\n", buffer ) ;
printf( "#fitted_ir_parameter_ratio: pi: -1 k0: -1 theta0: -1 k1: -1 theta1: -1\n" ) ;
printf( "#fitted_ir_parameter_cov: pi: -1 k0: -1 theta0: -1 k1: -1 theta1: -1\n" ) ;
int blockCnt = regions.exonBlocks.size() ;
for ( int i = 0 ; i < blockCnt ; ++i )
{
struct _block &e = regions.exonBlocks[i] ;
double avgDepth = (double)e.depthSum / ( e.end - e.start + 1 ) ;
printf( "%s %" PRId64 " %" PRId64 " %d %d %lf -1 -1 -1 -1 ", alignments.GetChromName( e.chrId ), e.start + 1, e.end + 1, e.leftType, e.rightType, avgDepth ) ;
int prevCnt = e.prevCnt ;
if ( i > 0 && e.start == regions.exonBlocks[i - 1].end + 1 &&
e.leftType == regions.exonBlocks[i - 1].rightType )
{
printf( "%d ", prevCnt + 1 ) ;
for ( j = 0 ; j < prevCnt ; ++j )
printf( "%" PRId64 " ", regions.exonBlocks[ e.prev[j] ].end + 1 ) ;
printf( "%" PRId64 " ", regions.exonBlocks[i - 1].end + 1 ) ;
}
else
{
printf( "%d ", prevCnt ) ;
for ( j = 0 ; j < prevCnt ; ++j )
printf( "%" PRId64 " ", regions.exonBlocks[ e.prev[j] ].end + 1 ) ;
}
int nextCnt = e.nextCnt ;
if ( i < blockCnt - 1 && e.end == regions.exonBlocks[i + 1].start - 1 &&
e.rightType == regions.exonBlocks[i + 1].leftType )
{
printf( "%d %" PRId64 " ", nextCnt + 1, regions.exonBlocks[i + 1].start + 1 ) ;
}
else
printf( "%d ", nextCnt ) ;
for ( j = 0 ; j < nextCnt ; ++j )
printf( "%" PRId64 " ", regions.exonBlocks[ e.next[j] ].start + 1 ) ;
printf( "\n" ) ;
}
return 0 ;
}
// Extract the blocks for different events.
int blockCnt = regions.exonBlocks.size() ;
std::vector<struct _block> irBlocks ; // The regions corresponds to intron retention events.
double *leftClassifier = new double[ blockCnt ] ;
double *rightClassifier = new double[ blockCnt ] ;
std::vector<struct _block> overhangBlocks ; //blocks like (...[ or ]...)
std::vector<struct _block> islandBlocks ; // blocks like (....)
for ( i = 0 ; i < blockCnt ; ++i )
{
int ltype = regions.exonBlocks[i].leftType ;
int rtype = regions.exonBlocks[i].rightType ;
leftClassifier[i] = -1 ;
rightClassifier[i] = -1 ;
//double avgDepth = (double)regions.exonBlocks[i].depthSum / ( regions.exonBlocks[i].end - regions.exonBlocks[i].start + 1 ) ;
if ( ltype == 2 && rtype == 1 )
{
// candidate intron retention.
// Note that when I compute the ratio, it is already made sure that the avgDepth>1.
double ratio = regions.PickLeftAndRightRatio( regions.exonBlocks[i] ) ;
//printf( "%lf %lf\n", regions.exonBlocks[i].leftRatio, regions.exonBlocks[i].rightRatio ) ;
if ( ratio > 0 )
{
regions.exonBlocks[i].ratio = ratio ;
irBlocks.push_back( regions.exonBlocks[i] ) ;
irBlocks[ irBlocks.size() - 1 ].contigId = i ;
}
}
else if ( ( ltype == 0 && rtype == 1 ) || ( ltype == 2 && rtype == 0 ) )
{
// subexons like (...[ or ]...)
double ratio ;
if ( ltype == 0 )
{
ratio = regions.exonBlocks[i].rightRatio ;
}
else if ( ltype == 2 )
{
ratio = regions.exonBlocks[i].leftRatio ;
}
if ( ratio > 0 )
{
regions.exonBlocks[i].ratio = ratio ;
overhangBlocks.push_back( regions.exonBlocks[i] ) ;
overhangBlocks[ overhangBlocks.size() - 1].contigId = i ;
}
}
else if ( ltype == 0 && rtype == 0 )
{
islandBlocks.push_back( regions.exonBlocks[i] ) ;
islandBlocks[ islandBlocks.size() - 1].contigId = i ;