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ProbabilisticModel.h
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/////////////////////////////////////////////////////////////////
// ProbabilisticModel.h
//
// Routines for (1) local pair-HMM posterior probability computations
// (2) double affine pair-HMM posterior probability computations
// (2) chained anchoring
// (3) maximum weight trace alignment
/////////////////////////////////////////////////////////////////
#ifndef PROBABILISTICMODEL_H
#define PROBABILISTICMODEL_H
#include <list>
#include <cmath>
#include <cstdio>
#include "SafeVector.h"
#include "ScoreType.h"
#include "SparseMatrix.h"
#include "MultiSequence.h"
using namespace std;
const int NumMatchStates = 1;
const int NumInsertStates = 2; // for double affine pair-HMM
const int NumMatrixTypes = NumMatchStates + NumInsertStates * 2;
/////////////////////////////////////////////////////////////////
// ProbabilisticModel
//
// Class for storing the parameters of a probabilistic model and
// performing different computations based on those parameters.
// In particular, this class handles the computation of
// posterior probabilities that may be used in alignment.
/////////////////////////////////////////////////////////////////
class ProbabilisticModel
{
float initialDistribution[NumMatrixTypes]; // holds the initial probabilities for each state
float transProb[NumMatrixTypes][NumMatrixTypes]; // holds all state-to-state transition probabilities for double affine pair-HMM
float matchProb[256][256]; // emission probabilities for match states
float insProb[256][NumMatrixTypes]; // emission probabilities for insert states
float local_transProb[3][3]; // holds central state-to-state transition probabilities for local pair-HMM
float random_transProb[2]; // holds flanking state-to-state transition probabilities for local pair-HMM
public:
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ProbabilisticModel()
//
// Constructor. Builds a new probabilistic model using the
// given parameters.
/////////////////////////////////////////////////////////////////
ProbabilisticModel (const VF &initDistribMat, const VF &gapOpen, const VF &gapExtend,
const VVF &emitPairs, const VF &emitSingle)
{
/**********************************
double affine pair-HMM:
initDistribMat[0,1,3,4]: initialization of starting states
gapOpen[0,2]: long and short open gap probabilities
gapExtend[0,2]:long and short extend gap probabilities
local pair-HMM:
initDistribMat[2]: leave from a flanking state probability
gapOpen[1]: central open gap probability
gapExtend[1]:central extend gap probability
***********************************/
//double affine pair-HMM
// build transition matrix
VVF transMat (NumMatrixTypes, VF (NumMatrixTypes, 0.0f));
transMat[0][0] = 1;
for (int i = 0; i < NumInsertStates; i++)
{
transMat[0][2*i+1] = gapOpen[2*i];
transMat[0][2*i+2] = gapOpen[2*i];
transMat[0][0] -= (gapOpen[2*i] + gapOpen[2*i]);
assert (transMat[0][0] > 0);
transMat[2*i+1][2*i+1] = gapExtend[2*i];
transMat[2*i+2][2*i+2] = gapExtend[2*i];
transMat[2*i+1][2*i+2] = 0;
transMat[2*i+2][2*i+1] = 0;
transMat[2*i+1][0] = 1 - gapExtend[2*i];
transMat[2*i+2][0] = 1 - gapExtend[2*i];
}
// create initial and transition probability matrices
for (int i = 0; i < NumMatrixTypes; i++)
{
initialDistribution[i] = LOG (initDistribMat[i]);
for (int j = 0; j < NumMatrixTypes; j++)
transProb[i][j] = LOG (transMat[i][j]);
}
//due to local model parameters' initilization
//need to correct initialDistribution[2]
initialDistribution[2] = LOG (initDistribMat[1]);
// create insertion and match probability matrices
for (int i = 0; i < 256; i++)
{
for (int j = 0; j < NumMatrixTypes; j++)
insProb[i][j] = LOG (emitSingle[i]);
for (int j = 0; j < 256; j++)
matchProb[i][j] = LOG (emitPairs[i][j]);
}
//local pair-HMM
// build transition matrix
VVF ltransMat (3, VF (3, 0.0f));
ltransMat[0][0] = 1;
ltransMat[0][1] = gapOpen[1];
ltransMat[0][2] = gapOpen[1];
ltransMat[0][0] -= (gapOpen[1] + gapOpen[1]);
assert (ltransMat[0][0] > 0);
ltransMat[1][1] = gapExtend[1];
ltransMat[2][2] = gapExtend[1];
ltransMat[1][2] = 0;
ltransMat[2][1] = 0;
ltransMat[1][0] = 1 - gapExtend[1];
ltransMat[2][0] = 1 - gapExtend[1];
// create initial and transition probability matrices
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
local_transProb[i][j] = LOG (ltransMat[i][j]);
}
// create initial and transition probability matrices
random_transProb[0] = LOG (initDistribMat[2]);//probability to leave from a randam state
random_transProb[1] = LOG (1-initDistribMat[2]);//probability to stay in a randam state
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeForwardMatrix()
//
// Computes a set of forward probability matrices for aligning
// seq1 and seq2.
//
// For efficiency reasons, a single-dimensional floating-point
// array is used here, with the following indexing scheme:
//
// forward[i + NumMatrixTypes * (j * (seq2Length+1) + k)]
// refers to the probability of aligning through j characters
// of the first sequence, k characters of the second sequence,
// and ending in state i.
// flag: 1 probcons, 0 local
/////////////////////////////////////////////////////////////////
VF *ComputeForwardMatrix (Sequence *seq1, Sequence *seq2, bool flag=true) const
{
assert (seq1);
assert (seq2);
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
// retrieve the points to the beginning of each sequence
SafeVector<char>::iterator iter1 = seq1->GetDataPtr();
SafeVector<char>::iterator iter2 = seq2->GetDataPtr();
// create matrix
VF *forwardPtr;
if(flag) forwardPtr = new VF (NumMatrixTypes * (seq1Length+1) * (seq2Length+1), LOG_ZERO);
else forwardPtr = new VF (3 * (seq1Length+1) * (seq2Length+1), LOG_ZERO);
assert (forwardPtr);
VF &forward = *forwardPtr;
// initialization condition
if(flag)
{
forward[0 + NumMatrixTypes * (1 * (seq2Length+1) + 1)] =
initialDistribution[0] + matchProb[(unsigned char) iter1[1]][(unsigned char) iter2[1]];
for (int k = 0; k < NumInsertStates; k++)
{
forward[2*k+1 + NumMatrixTypes * (1 * (seq2Length+1) + 0)] =
initialDistribution[2*k+1] + insProb[(unsigned char) iter1[1]][k];
forward[2*k+2 + NumMatrixTypes * (0 * (seq2Length+1) + 1)] =
initialDistribution[2*k+2] + insProb[(unsigned char) iter2[1]][k];
}
}
// remember offset for each index combination
int ij = 0;
int i1j = -seq2Length - 1;
int ij1 = -1;
int i1j1 = -seq2Length - 2;
if(flag)
{
ij *= NumMatrixTypes;
i1j *= NumMatrixTypes;
ij1 *= NumMatrixTypes;
i1j1 *= NumMatrixTypes;
}
else
{
ij *= 3;
i1j *= 3;
ij1 *= 3;
i1j1 *= 3;
}
// compute forward scores
for (int i = 0; i <= seq1Length; i++)
{
unsigned char c1 = (i == 0) ? '~' : (unsigned char) iter1[i];
for (int j = 0; j <= seq2Length; j++)
{
unsigned char c2 = (j == 0) ? '~' : (unsigned char) iter2[j];
//local
if(i == 1 && j == 1 && !flag) forward[0 + ij] =
matchProb[c1][c2] - insProb[c1][0] - insProb[c2][0] - 2*random_transProb[1];
if (i > 1 || j > 1)
{
if (i > 0 && j > 0)
{
if(flag)
{
forward[0 + ij] = forward[0 + i1j1] + transProb[0][0];
for (int k = 1; k < NumMatrixTypes; k++)
LOG_PLUS_EQUALS (forward[0 + ij], forward[k + i1j1] + transProb[k][0]);
forward[0 + ij] += matchProb[c1][c2];
}
//local
else
{
forward[0 + ij] = matchProb[c1][c2] - insProb[c1][0] - insProb[c2][0] - 2*random_transProb[1];
for (int k = 0; k < 3; k++)
LOG_PLUS_EQUALS (forward[0 + ij], matchProb[c1][c2] - insProb[c1][0] - insProb[c2][0] +
forward[k + i1j1] + local_transProb[k][0] - 2*random_transProb[1]);
}
}
if (i > 0)
{
if(flag)
{
for (int k = 0; k < NumInsertStates; k++)
forward[2*k+1 + ij] = insProb[c1][k] +
LOG_ADD (forward[0 + i1j] + transProb[0][2*k+1],
forward[2*k+1 + i1j] + transProb[2*k+1][2*k+1]);
}
//local
else
{
forward[1 + ij] = LOG_ADD (forward[0 + i1j] + local_transProb[0][1] - random_transProb[1],
forward[1 + i1j] + local_transProb[1][1] - random_transProb[1]);
}
}
if (j > 0)
{
if(flag)
{
for (int k = 0; k < NumInsertStates; k++)
forward[2*k+2 + ij] = insProb[c2][k] +
LOG_ADD (forward[0 + ij1] + transProb[0][2*k+2],
forward[2*k+2 + ij1] + transProb[2*k+2][2*k+2]);
}
//local
else
{
forward[2 + ij] = LOG_ADD (forward[0 + ij1] + local_transProb[0][2] - random_transProb[1],
forward[2 + ij1] + local_transProb[2][2] - random_transProb[1]);
}
}
}
if(flag)
{
ij += NumMatrixTypes;
i1j += NumMatrixTypes;
ij1 += NumMatrixTypes;
i1j1 += NumMatrixTypes;
}
else
{
ij += 3;
i1j += 3;
ij1 += 3;
i1j1 += 3;
}
}
}
return forwardPtr;
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeBackwardMatrix()
//
// Computes a set of backward probability matrices for aligning
// seq1 and seq2.
//
// For efficiency reasons, a single-dimensional floating-point
// array is used here, with the following indexing scheme:
//
// backward[i + NumMatrixTypes * (j * (seq2Length+1) + k)]
// refers to the probability of starting in state i and
// aligning from character j+1 to the end of the first
// sequence and from character k+1 to the end of the second
// sequence.
/////////////////////////////////////////////////////////////////
VF *ComputeBackwardMatrix (Sequence *seq1, Sequence *seq2, bool flag=true) const
{
assert (seq1);
assert (seq2);
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
SafeVector<char>::iterator iter1 = seq1->GetDataPtr();
SafeVector<char>::iterator iter2 = seq2->GetDataPtr();
// create matrix
VF *backwardPtr;
if(flag) backwardPtr = new VF (NumMatrixTypes * (seq1Length+1) * (seq2Length+1), LOG_ZERO);
else backwardPtr = new VF (3 * (seq1Length+1) * (seq2Length+1), LOG_ZERO);
assert (backwardPtr);
VF &backward = *backwardPtr;
// initialization condition
if(flag)
{
for (int k = 0; k < NumMatrixTypes; k++)
backward[NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1) + k] = initialDistribution[k];
}
// remember offset for each index combination
int ij = (seq1Length+1) * (seq2Length+1) - 1;
int i1j = ij + seq2Length + 1;
int ij1 = ij + 1;
int i1j1 = ij + seq2Length + 2;
if(flag)
{
ij *= NumMatrixTypes;
i1j *= NumMatrixTypes;
ij1 *= NumMatrixTypes;
i1j1 *= NumMatrixTypes;
}
else
{
ij *= 3;
i1j *= 3;
ij1 *= 3;
i1j1 *= 3;
}
// compute backward scores
for (int i = seq1Length; i >= 0; i--)
{
unsigned char c1 = (i == seq1Length) ? '~' : (unsigned char) iter1[i+1];
for (int j = seq2Length; j >= 0; j--)
{
unsigned char c2 = (j == seq2Length) ? '~' : (unsigned char) iter2[j+1];
if(!flag) backward[0 + ij] = LOG_ONE;//local
if (i < seq1Length && j < seq2Length)
{
if(flag)
{
const float ProbXY = backward[0 + i1j1] + matchProb[c1][c2];
for (int k = 0; k < NumMatrixTypes; k++)
LOG_PLUS_EQUALS (backward[k + ij], ProbXY + transProb[k][0]);
}
//local
else
{
const float ProbXY = backward[0 + i1j1] + matchProb[c1][c2] - insProb[c1][0] - insProb[c2][0];
for (int k = 0; k < 3; k++)
LOG_PLUS_EQUALS (backward[k + ij], ProbXY + local_transProb[k][0] - 2*random_transProb[1] );
}
}
if (i < seq1Length)
{
if(flag)
{
for (int k = 0; k < NumInsertStates; k++)
{
LOG_PLUS_EQUALS (backward[0 + ij], backward[2*k+1 + i1j] + insProb[c1][k] + transProb[0][2*k+1]);
LOG_PLUS_EQUALS (backward[2*k+1 + ij], backward[2*k+1 + i1j] + insProb[c1][k] + transProb[2*k+1][2*k+1]);
}
}
//local
else
{
LOG_PLUS_EQUALS (backward[0 + ij], backward[1 + i1j] + local_transProb[0][1] - random_transProb[1]);
LOG_PLUS_EQUALS (backward[1 + ij], backward[1 + i1j] + local_transProb[1][1] - random_transProb[1]);
}
}
if (j < seq2Length)
{
if(flag)
{
for (int k = 0; k < NumInsertStates; k++)
{
LOG_PLUS_EQUALS (backward[0 + ij], backward[2*k+2 + ij1] + insProb[c2][k] + transProb[0][2*k+2]);
LOG_PLUS_EQUALS (backward[2*k+2 + ij], backward[2*k+2 + ij1] + insProb[c2][k] + transProb[2*k+2][2*k+2]);
}
}
//local
else
{
LOG_PLUS_EQUALS (backward[0 + ij], backward[2 + ij1] + local_transProb[0][2] - random_transProb[1]);
LOG_PLUS_EQUALS (backward[2 + ij], backward[2 + ij1] + local_transProb[2][2] - random_transProb[1]);
}
}
if(flag)
{
ij -= NumMatrixTypes;
i1j -= NumMatrixTypes;
ij1 -= NumMatrixTypes;
i1j1 -= NumMatrixTypes;
}
else
{
ij -= 3;
i1j -= 3;
ij1 -= 3;
i1j1 -= 3;
}
}
}
return backwardPtr;
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeTotalProbability()
//
// Computes the total probability of an alignment given
// the forward and backward matrices.
// flag: 1 probcons, 0 local
/////////////////////////////////////////////////////////////////
float ComputeTotalProbability (Sequence *seq1, Sequence *seq2,
const VF &forward, const VF &backward, bool flag=true) const
{
// compute total probability
float totalForwardProb = LOG_ZERO;
float totalBackwardProb = LOG_ZERO;
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
if(flag)
{
for (int k = 0; k < NumMatrixTypes; k++)
{
LOG_PLUS_EQUALS (totalForwardProb,
forward[k + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)] +
backward[k + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)]);
}
totalBackwardProb =
forward[0 + NumMatrixTypes * (1 * (seq2Length+1) + 1)] +
backward[0 + NumMatrixTypes * (1 * (seq2Length+1) + 1)];
for (int k = 0; k < NumInsertStates; k++)
{
LOG_PLUS_EQUALS (totalBackwardProb,
forward[2*k+1 + NumMatrixTypes * (1 * (seq2Length+1) + 0)] +
backward[2*k+1 + NumMatrixTypes * (1 * (seq2Length+1) + 0)]);
LOG_PLUS_EQUALS (totalBackwardProb,
forward[2*k+2 + NumMatrixTypes * (0 * (seq2Length+1) + 1)] +
backward[2*k+2 + NumMatrixTypes * (0 * (seq2Length+1) + 1)]);
}
}
else
{
SafeVector<char>::iterator iter1 = seq1->GetDataPtr();
SafeVector<char>::iterator iter2 = seq2->GetDataPtr();
int ij = 0;
for (int i = 0; i <= seq1Length; i++)
{
unsigned char c1 = (i == 0) ? '~' : (unsigned char) iter1[i];
for (int j = 0; j <= seq2Length; j++)
{
unsigned char c2 = (j == 0) ? '~' : (unsigned char) iter2[j];
if(i>0&&j>0)
{
LOG_PLUS_EQUALS (totalForwardProb,forward[ij]);
LOG_PLUS_EQUALS (totalBackwardProb,backward[ij] + matchProb[c1][c2]
- insProb[c1][0] - insProb[c2][0] - 2*random_transProb[1]);
}
ij += 3;
}
}
}
return (totalForwardProb + totalBackwardProb) / 2;
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputePosteriorMatrix()
//
// Computes the posterior probability matrix based on
// the forward and backward matrices.
// flag: 1 probcons, 0 local
/////////////////////////////////////////////////////////////////
VF *ComputePosteriorMatrix (Sequence *seq1, Sequence *seq2,
const VF &forward, const VF &backward, bool flag=true) const
{
assert (seq1);
assert (seq2);
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
float totalProb = ComputeTotalProbability (seq1, seq2,forward, backward, flag);
// compute posterior matrices
VF *posteriorPtr = new VF((seq1Length+1) * (seq2Length+1));
assert (posteriorPtr);
VF &posterior = *posteriorPtr;
int ij = 0;
VF::iterator ptr = posterior.begin();
for (int i = 0; i <= seq1Length; i++)
{
for (int j = 0; j <= seq2Length; j++)
{
*(ptr++) = EXP (min (LOG_ONE, forward[ij] + backward[ij] - totalProb));
if(flag) ij += NumMatrixTypes;
else ij += 3;
}
}
posterior[0] = 0;
return posteriorPtr;
}
/*
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeExpectedCounts()
//
// Computes the expected counts for the various transitions.
/////////////////////////////////////////////////////////////////
VVF *ComputeExpectedCounts () const {
assert (seq1);
assert (seq2);
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
SafeVector<char>::iterator iter1 = seq1->GetDataPtr();
SafeVector<char>::iterator iter2 = seq2->GetDataPtr();
// compute total probability
float totalProb = ComputeTotalProbability (seq1Length, seq2Length,
forward, backward);
// initialize expected counts
VVF *countsPtr = new VVF(NumMatrixTypes + 1, VF(NumMatrixTypes, LOG_ZERO)); assert (countsPtr);
VVF &counts = *countsPtr;
// remember offset for each index combination
int ij = 0;
int i1j = -seq2Length - 1;
int ij1 = -1;
int i1j1 = -seq2Length - 2;
ij *= NumMatrixTypes;
i1j *= NumMatrixTypes;
ij1 *= NumMatrixTypes;
i1j1 *= NumMatrixTypes;
// compute expected counts
for (int i = 0; i <= seq1Length; i++){
unsigned char c1 = (i == 0) ? '~' : (unsigned char) iter1[i];
for (int j = 0; j <= seq2Length; j++){
unsigned char c2 = (j == 0) ? '~' : (unsigned char) iter2[j];
if (i > 0 && j > 0){
for (int k = 0; k < NumMatrixTypes; k++)
LOG_PLUS_EQUALS (counts[k][0],
forward[k + i1j1] + transProb[k][0] +
matchProb[c1][c2] + backward[0 + ij]);
}
if (i > 0){
for (int k = 0; k < NumInsertStates; k++){
LOG_PLUS_EQUALS (counts[0][2*k+1],
forward[0 + i1j] + transProb[0][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
LOG_PLUS_EQUALS (counts[2*k+1][2*k+1],
forward[2*k+1 + i1j] + transProb[2*k+1][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
}
}
if (j > 0){
for (int k = 0; k < NumInsertStates; k++){
LOG_PLUS_EQUALS (counts[0][2*k+2],
forward[0 + ij1] + transProb[0][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
LOG_PLUS_EQUALS (counts[2*k+2][2*k+2],
forward[2*k+2 + ij1] + transProb[2*k+2][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
}
}
ij += NumMatrixTypes;
i1j += NumMatrixTypes;
ij1 += NumMatrixTypes;
i1j1 += NumMatrixTypes;
}
}
// scale all expected counts appropriately
for (int i = 0; i < NumMatrixTypes; i++)
for (int j = 0; j < NumMatrixTypes; j++)
counts[i][j] -= totalProb;
}
*/
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeNewParameters()
//
// Computes a new parameter set based on the expected counts
// given.
/////////////////////////////////////////////////////////////////
void ComputeNewParameters (Sequence *seq1, Sequence *seq2,
const VF &forward, const VF &backward,
VF &initDistribMat, VF &gapOpen,
VF &gapExtend, VVF &emitPairs, VF &emitSingle, bool enableTrainEmissions) const
{
assert (seq1);
assert (seq2);
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
SafeVector<char>::iterator iter1 = seq1->GetDataPtr();
SafeVector<char>::iterator iter2 = seq2->GetDataPtr();
// compute total probability
float totalProb = ComputeTotalProbability (seq1, seq2,
forward, backward);
// initialize expected counts
VVF transCounts (NumMatrixTypes, VF (NumMatrixTypes, LOG_ZERO));
VF initCounts (NumMatrixTypes, LOG_ZERO);
VVF pairCounts (256, VF (256, LOG_ZERO));
VF singleCounts (256, LOG_ZERO);
// remember offset for each index combination
int ij = 0;
int i1j = -seq2Length - 1;
int ij1 = -1;
int i1j1 = -seq2Length - 2;
ij *= NumMatrixTypes;
i1j *= NumMatrixTypes;
ij1 *= NumMatrixTypes;
i1j1 *= NumMatrixTypes;
// compute initial distribution posteriors
initCounts[0] = LOG_ADD (forward[0 + NumMatrixTypes * (1 * (seq2Length+1) + 1)] +
backward[0 + NumMatrixTypes * (1 * (seq2Length+1) + 1)],
forward[0 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)] +
backward[0 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)]);
for (int k = 0; k < NumInsertStates; k++)
{
initCounts[2*k+1] = LOG_ADD (forward[2*k+1 + NumMatrixTypes * (1 * (seq2Length+1) + 0)] +
backward[2*k+1 + NumMatrixTypes * (1 * (seq2Length+1) + 0)],
forward[2*k+1 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)] +
backward[2*k+1 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)]);
initCounts[2*k+2] = LOG_ADD (forward[2*k+2 + NumMatrixTypes * (0 * (seq2Length+1) + 1)] +
backward[2*k+2 + NumMatrixTypes * (0 * (seq2Length+1) + 1)],
forward[2*k+2 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)] +
backward[2*k+2 + NumMatrixTypes * ((seq1Length+1) * (seq2Length+1) - 1)]);
}
// compute expected counts
for (int i = 0; i <= seq1Length; i++)
{
unsigned char c1 = (i == 0) ? '~' : (unsigned char) toupper(iter1[i]);
for (int j = 0; j <= seq2Length; j++)
{
unsigned char c2 = (j == 0) ? '~' : (unsigned char) toupper(iter2[j]);
if (i > 0 && j > 0)
{
if (enableTrainEmissions && i == 1 && j == 1)
{
LOG_PLUS_EQUALS (pairCounts[c1][c2],
initialDistribution[0] + matchProb[c1][c2] + backward[0 + ij]);
LOG_PLUS_EQUALS (pairCounts[c2][c1],
initialDistribution[0] + matchProb[c2][c1] + backward[0 + ij]);
}
for (int k = 0; k < NumMatrixTypes; k++)
{
LOG_PLUS_EQUALS (transCounts[k][0],
forward[k + i1j1] + transProb[k][0] +
matchProb[c1][c2] + backward[0 + ij]);
if (enableTrainEmissions && (i != 1 || j != 1))
{
LOG_PLUS_EQUALS (pairCounts[c1][c2],
forward[k + i1j1] + transProb[k][0] +
matchProb[c1][c2] + backward[0 + ij]);
LOG_PLUS_EQUALS (pairCounts[c2][c1],
forward[k + i1j1] + transProb[k][0] +
matchProb[c2][c1] + backward[0 + ij]);
}
}
}
if (i > 0)
{
for (int k = 0; k < NumInsertStates; k++)
{
LOG_PLUS_EQUALS (transCounts[0][2*k+1],
forward[0 + i1j] + transProb[0][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
LOG_PLUS_EQUALS (transCounts[2*k+1][2*k+1],
forward[2*k+1 + i1j] + transProb[2*k+1][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
if (enableTrainEmissions)
{
if (i == 1 && j == 0)
{
LOG_PLUS_EQUALS (singleCounts[c1],
initialDistribution[2*k+1] + insProb[c1][k] + backward[2*k+1 + ij]);
}
else
{
LOG_PLUS_EQUALS (singleCounts[c1],
forward[0 + i1j] + transProb[0][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
LOG_PLUS_EQUALS (singleCounts[c1],
forward[2*k+1 + i1j] + transProb[2*k+1][2*k+1] +
insProb[c1][k] + backward[2*k+1 + ij]);
}
}
}
}
if (j > 0)
{
for (int k = 0; k < NumInsertStates; k++)
{
LOG_PLUS_EQUALS (transCounts[0][2*k+2],
forward[0 + ij1] + transProb[0][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
LOG_PLUS_EQUALS (transCounts[2*k+2][2*k+2],
forward[2*k+2 + ij1] + transProb[2*k+2][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
if (enableTrainEmissions)
{
if (i == 0 && j == 1)
{
LOG_PLUS_EQUALS (singleCounts[c2],
initialDistribution[2*k+2] + insProb[c2][k] + backward[2*k+2 + ij]);
}
else
{
LOG_PLUS_EQUALS (singleCounts[c2],
forward[0 + ij1] + transProb[0][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
LOG_PLUS_EQUALS (singleCounts[c2],
forward[2*k+2 + ij1] + transProb[2*k+2][2*k+2] +
insProb[c2][k] + backward[2*k+2 + ij]);
}
}
}
}
ij += NumMatrixTypes;
i1j += NumMatrixTypes;
ij1 += NumMatrixTypes;
i1j1 += NumMatrixTypes;
}
}
// scale all expected counts appropriately
for (int i = 0; i < NumMatrixTypes; i++)
{
initCounts[i] -= totalProb;
for (int j = 0; j < NumMatrixTypes; j++)
transCounts[i][j] -= totalProb;
}
if (enableTrainEmissions)
{
for (int i = 0; i < 256; i++)
{
for (int j = 0; j < 256; j++)
pairCounts[i][j] -= totalProb;
singleCounts[i] -= totalProb;
}
}
// compute new initial distribution
float totalInitDistribCounts = 0;
for (int i = 0; i < NumMatrixTypes; i++)
totalInitDistribCounts += exp (initCounts[i]); // should be 2
initDistribMat[0] = min (1.0f, max (0.0f, (float) exp (initCounts[0]) / totalInitDistribCounts));
for (int k = 0; k < NumInsertStates; k++)
{
float val = (exp (initCounts[2*k+1]) + exp (initCounts[2*k+2])) / 2;
initDistribMat[2*k+1] = initDistribMat[2*k+2] = min (1.0f, max (0.0f, val / totalInitDistribCounts));
}
// compute total counts for match state
float inMatchStateCounts = 0;
for (int i = 0; i < NumMatrixTypes; i++)
inMatchStateCounts += exp (transCounts[0][i]);
for (int i = 0; i < NumInsertStates; i++)
{
// compute total counts for gap state
float inGapStateCounts =
exp (transCounts[2*i+1][0]) +
exp (transCounts[2*i+1][2*i+1]) +
exp (transCounts[2*i+2][0]) +
exp (transCounts[2*i+2][2*i+2]);
gapOpen[2*i] = gapOpen[2*i+1] =
(exp (transCounts[0][2*i+1]) +
exp (transCounts[0][2*i+2])) /
(2 * inMatchStateCounts);
gapExtend[2*i] = gapExtend[2*i+1] =
(exp (transCounts[2*i+1][2*i+1]) +
exp (transCounts[2*i+2][2*i+2])) /
inGapStateCounts;
}
if (enableTrainEmissions)
{
float totalPairCounts = 0;
float totalSingleCounts = 0;
for (int i = 0; i < 256; i++)
{
for (int j = 0; j <= i; j++)
totalPairCounts += exp (pairCounts[j][i]);
totalSingleCounts += exp (singleCounts[i]);
}
for (int i = 0; i < 256; i++) if (!islower ((char) i))
{
int li = (int)((unsigned char) tolower ((char) i));
for (int j = 0; j <= i; j++) if (!islower ((char) j))
{
int lj = (int)((unsigned char) tolower ((char) j));
emitPairs[i][j] = emitPairs[i][lj] = emitPairs[li][j] = emitPairs[li][lj] =
emitPairs[j][i] = emitPairs[j][li] = emitPairs[lj][i] = emitPairs[lj][li] = exp(pairCounts[j][i]) / totalPairCounts;
}
emitSingle[i] = emitSingle[li] = exp(singleCounts[i]) / totalSingleCounts;
}
}
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeAlignment()
//
// Computes an alignment based on the given posterior matrix.
// This is done by finding the maximum summing path (or
// maximum weight trace) through the posterior matrix. The
// final alignment is returned as a pair consisting of:
// (1) a string (e.g., XXXBBXXXBBBBBBYYYYBBB) where X's and
// denote insertions in one of the two sequences and
// B's denote that both sequences are present (i.e.
// matches).
// (2) a float indicating the sum achieved
/////////////////////////////////////////////////////////////////
pair<SafeVector<char> *, float> ComputeAlignment (int seq1Length, int seq2Length,
const VF &posterior) const
{
float *twoRows = new float[(seq2Length+1)*2];
assert (twoRows);
float *oldRow = twoRows;
float *newRow = twoRows + seq2Length + 1;
char *tracebackMatrix = new char[(seq1Length+1)*(seq2Length+1)];
assert (tracebackMatrix);
char *tracebackPtr = tracebackMatrix;
VF::const_iterator posteriorPtr = posterior.begin() + seq2Length + 1;
// initialization
for (int i = 0; i <= seq2Length; i++)
{
oldRow[i] = 0;
*(tracebackPtr++) = 'L';
}
// fill in matrix
for (int i = 1; i <= seq1Length; i++)
{
// initialize left column
newRow[0] = 0;
posteriorPtr++;
*(tracebackPtr++) = 'U';
// fill in rest of row
for (int j = 1; j <= seq2Length; j++)
{
ChooseBestOfThree (*(posteriorPtr++) + oldRow[j-1], newRow[j-1], oldRow[j],
'D', 'L', 'U', &newRow[j], tracebackPtr++);
}
// swap rows
float *temp = oldRow;
oldRow = newRow;
newRow = temp;
}
// store best score
float total = oldRow[seq2Length];
delete [] twoRows;
// compute traceback
SafeVector<char> *alignment = new SafeVector<char>;
assert (alignment);
int r = seq1Length, c = seq2Length;
while (r != 0 || c != 0)
{
char ch = tracebackMatrix[r*(seq2Length+1) + c];
switch (ch)
{
case 'L':
c--;
alignment->push_back ('Y');
break;
case 'U':
r--;
alignment->push_back ('X');
break;
case 'D':
c--;
r--;
alignment->push_back ('B');
break;
default:
assert (false);
}
}
delete [] tracebackMatrix;
reverse (alignment->begin(), alignment->end());
return make_pair(alignment, total);
}
/////////////////////////////////////////////////////////////////
// ProbabilisticModel::ComputeAlignmentWithGapPenalties()
//
// Similar to ComputeAlignment() except with gap penalties.
/////////////////////////////////////////////////////////////////
pair<SafeVector<char> *, float> ComputeAlignmentWithGapPenalties (MultiSequence *align1,
MultiSequence *align2,
const VF &posterior, int numSeqs1,
int numSeqs2,
float gapOpenPenalty,
float gapContinuePenalty) const
{
int seq1Length = align1->GetSequence(0)->GetLength();
int seq2Length = align2->GetSequence(0)->GetLength();
SafeVector<SafeVector<char>::iterator > dataPtrs1 (align1->GetNumSequences());
SafeVector<SafeVector<char>::iterator > dataPtrs2 (align2->GetNumSequences());
// grab character data
for (int i = 0; i < align1->GetNumSequences(); i++)
dataPtrs1[i] = align1->GetSequence(i)->GetDataPtr();
for (int i = 0; i < align2->GetNumSequences(); i++)
dataPtrs2[i] = align2->GetSequence(i)->GetDataPtr();
// the number of active sequences at any given column is defined to be the
// number of non-gap characters in that column; the number of gap opens at
// any given column is defined to be the number of gap characters in that
// column where the previous character in the respective sequence was not
// a gap
SafeVector<int> numActive1 (seq1Length+1), numGapOpens1 (seq1Length+1);
SafeVector<int> numActive2 (seq2Length+1), numGapOpens2 (seq2Length+1);
// compute number of active sequences and gap opens for each group
for (int i = 0; i < align1->GetNumSequences(); i++)
{