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MSA.cpp
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MSA.cpp
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#include <string>
#include <sstream>
#include <iomanip>
#include <iostream>
#include <list>
#include <set>
#include <algorithm>
#include <climits>
#include <cstdio>
#include <cstdlib>
#include <cerrno>
#include <iomanip>
#include "MSA.h"
#include "MSAClusterTree.h"
#include "Defaults.h"
#include "CalculateFeatures.h"
string parametersInputFilename = "";
bool enableAlignOrder = false;
int numConsistencyReps = 2;
int numPreTrainingReps = 0;
int numIterativeRefinementReps = 100;
float cutoff = 0;
int first_step = 0;
bool get_features = false;
int second_step = 0;
int third_step = 0;
VF initDistrib(NumMatrixTypes);
VF gapOpen(2 * NumInsertStates);
VF gapExtend(2 * NumInsertStates);
VVF emitPairs(256, VF(256, 1e-10));//subsititution matrix for double affine pair-HMM
VF emitSingle(256, 1e-5);//subsititution matrix for double affine pair-HMM
string alphabet = alphabetDefault;
///////////////////////////////
// global pair-HMM variables
//////////////////////////////
float g_gap_open1, g_gap_open2, g_gap_ext1, g_gap_ext2;
char *aminos, *bases, matrixtype[20] = "gonnet_160";
int subst_index[26];
double sub_matrix[26][26]; //subsititution matrix for global pair-HMM
double normalized_matrix[26][26];// be used to compute sequences' similarity score
int firstread = 0; //this makes sure that matrices are read only once
float TEMPERATURE = 5;
int MATRIXTYPE = 160;
float GAPOPEN = 0;
float GAPEXT = 0;
//argument support
typedef struct
{
char input[30];
int matrix;
int N;
float T;
float beta;
char opt; //can be 'P' or 'M'
float gapopen;
float gapext;
} argument_decl;
argument_decl argument;
extern void init_arguments();
MSA::MSA(int argc, char* argv[])
{
//parse program parameters
SafeVector<string> sequenceNames = ParseParams(argc, argv);
//initialize arguments for partition function
init_arguments();
ReadParameters();
//read the input sequences
MultiSequence *sequences = new MultiSequence();
assert(sequences);
for (int i = 0; i < (int) sequenceNames.size(); i++)
{
sequences->LoadMFA(sequenceNames[i], true);
}
//allocate space for sequence weights
this->seqsWeights = new int[sequences->GetNumSequences()];
/**************************************
// average percent identity
// Divergent(<=25%) levelid = 0
// Medium(25%-40%) levelid = 1
// Similar(40%-70%) levelid = 2
// High Similar(>70%) levelid = 3
***************************************/
if(get_features == true)
{
CalculateFeatures *calculate = new CalculateFeatures(sequences, ProbabilisticModel(initDistrib, gapOpen, gapExtend, emitPairs,emitSingle));
cout << calculate->getFeatures() << endl;
return;
}
// now, we can perform the alignments and write them out
MultiSequence *alignment = doAlign(sequences,
ProbabilisticModel(initDistrib, gapOpen, gapExtend, emitPairs,emitSingle), first_step, second_step, third_step);
alignment->WriteMFA(*alignOutFile);
//release resources
delete alignment;
delete[] this->seqsWeights;
delete sequences;
}
MSA::~MSA()
{
/*close the output file*/
if (alignOutFileName.length() > 0)
{
((std::ofstream*) alignOutFile)->close();
}
}
/////////////////////////////////////////////////////////////////
// doAlign()
//
// First computes all pairwise posterior probability matrices.
// Then, computes new parameters if training, or a final
// alignment, otherwise.
/////////////////////////////////////////////////////////////////
extern VF *ComputePostProbs(int a, int b, string seq1, string seq2);
MultiSequence* MSA::doAlign(MultiSequence *sequences,
const ProbabilisticModel &model, int first_step, int second_step, int third_step)
{
assert(sequences);
//get the number of sequences
const int numSeqs = sequences->GetNumSequences();
//create distance matrix
VVF distances(numSeqs, VF(numSeqs, 0));
//creat sparseMatrices
SafeVector<SafeVector<SparseMatrix *> > sparseMatrices(numSeqs,
SafeVector<SparseMatrix *>(numSeqs, NULL));
// do all pairwise alignments for posterior probability matrices
for (int a = 0; a < numSeqs - 1; a++)
{
for (int b = a + 1; b < numSeqs; b++)
{
Sequence *seq1 = sequences->GetSequence(a);
Sequence *seq2 = sequences->GetSequence(b);
//posterior probability matrix
VF* posterior;
if(first_step == 0)
{
//cerr << first_step << "Step 1: using Double Affine Pair-HMMs." << endl;
// compute forward and backward probabilities
VF *forward = model.ComputeForwardMatrix(seq1, seq2);
assert(forward);
VF *backward = model.ComputeBackwardMatrix(seq1, seq2);
assert(backward);
// compute posterior probability
posterior = model.ComputePosteriorMatrix(seq1, seq2, *forward,*backward);
delete forward;
delete backward;
}
else if(first_step == 1){
//cerr << first_step << "Step 1: using Partition Function." << endl;
posterior = ::ComputePostProbs(a, b, seq1->GetString(),seq2->GetString());
}
//divergent use combined model
else if(first_step == 2)
{
// cerr << first_step << "Step 1: using MSAProbs Model." << endl;
//double affine pair-HMM
// compute forward and backward probabilities
VF *forward = model.ComputeForwardMatrix(seq1, seq2);
assert(forward);
VF *backward = model.ComputeBackwardMatrix(seq1, seq2);
assert(backward);
// compute posterior probability
VF *double_posterior = model.ComputePosteriorMatrix(seq1, seq2, *forward,*backward);
assert(double_posterior);
delete forward;
delete backward;
// compute posterior probability
posterior = ::ComputePostProbs(a, b, seq1->GetString(),seq2->GetString());
assert(posterior);
//combined model
//merge probalign + local + probcons
VF::iterator ptr1 = double_posterior->begin();
VF::iterator ptr = posterior->begin();
for (int i = 0; i <= seq1->GetLength(); i++)
{
for (int j = 0; j <= seq2->GetLength(); j++)
{
float v1 = *ptr1;
float v3 = *ptr;
*ptr = sqrt((v1*v1 + v3*v3)/2);
ptr1++;
ptr++;
}
}
delete double_posterior;
}
//divergent use combined model
else
{
// cerr << first_step << "Step 1: using Combination Model." << endl;
//double affine pair-HMM
// compute forward and backward probabilities
VF *forward = model.ComputeForwardMatrix(seq1, seq2);
assert(forward);
VF *backward = model.ComputeBackwardMatrix(seq1, seq2);
assert(backward);
// compute posterior probability
VF *double_posterior = model.ComputePosteriorMatrix(seq1, seq2, *forward,*backward);
assert(double_posterior);
delete forward;
delete backward;
// compute posterior probability
VF *global_posterior = ::ComputePostProbs(a, b, seq1->GetString(),seq2->GetString());
assert(global_posterior);
//local pair-HMM
// compute forward and backward probabilities
forward = model.ComputeForwardMatrix(seq1, seq2, false);
assert(forward);
backward = model.ComputeBackwardMatrix(seq1, seq2, false);
assert(backward);
// compute posterior probability
posterior = model.ComputePosteriorMatrix(seq1, seq2, *forward,*backward, false);
assert(posterior);
delete forward;
delete backward;
//combined model
//merge probalign + local + probcons
VF::iterator ptr1 = double_posterior->begin();
VF::iterator ptr2 = global_posterior->begin();
VF::iterator ptr = posterior->begin();
for (int i = 0; i <= seq1->GetLength(); i++)
{
for (int j = 0; j <= seq2->GetLength(); j++)
{
float v1 = *ptr1;
float v2 = *ptr2;
float v3 = *ptr;
*ptr = sqrt((v1*v1 + v2*v2 + v3*v3)/3);
ptr1++;
ptr2++;
ptr++;
}
}
delete double_posterior;
delete global_posterior;
}
assert(posterior);
// perform the pairwise sequence alignment
pair<SafeVector<char> *, float> alignment = model.ComputeAlignment(
seq1->GetLength(), seq2->GetLength(), *posterior);
//compute expected accuracy
distances[a][b] = distances[b][a] = 1.0f - alignment.second
/ min(seq1->GetLength(), seq2->GetLength());
// compute sparse representations
sparseMatrices[a][b] = new SparseMatrix(seq1->GetLength(),
seq2->GetLength(), *posterior);
sparseMatrices[b][a] = NULL;
delete posterior;
delete alignment.first;
}
}
//create the guide tree
this->tree = new MSAClusterTree(this, distances, numSeqs);
this->tree->create(second_step);
// perform the consistency transformation the desired number of times
float* fweights = new float[numSeqs];
for (int r = 0; r < numSeqs; r++)
{
fweights[r] = ((float) seqsWeights[r]) / INT_MULTIPLY;
fweights[r] *= 10;
}
for (int r = 0; r < numConsistencyReps; r++)
{
SafeVector<SafeVector<SparseMatrix *> > newSparseMatrices;
if (third_step == 1){
//cerr << "Step 3: using weighted Probabilistic Comsistency Transformation." << endl;
newSparseMatrices = DoRelaxation(fweights, sequences, sparseMatrices);
}else{
//cerr << "Step 3: using unweighted Probabilistic Comsistency Transformation." << endl;
newSparseMatrices = DoRelaxation(sequences, sparseMatrices);
}
// now replace the old posterior matrices
for (int i = 0; i < numSeqs; i++)
{
for (int j = 0; j < numSeqs; j++)
{
delete sparseMatrices[i][j];
sparseMatrices[i][j] = newSparseMatrices[i][j];
}
}
}
delete[] fweights;
//compute the final multiple sequence alignment
MultiSequence *finalAlignment = ComputeFinalAlignment(this->tree, sequences,
sparseMatrices, model, first_step);
//destroy the guide tree
delete this->tree;
this->tree = 0;
// delete sparse matrices
for (int a = 0; a < numSeqs - 1; a++)
{
for (int b = a + 1; b < numSeqs; b++)
{
delete sparseMatrices[a][b];
delete sparseMatrices[b][a];
}
}
return finalAlignment;
}
/////////////////////////////////////////////////////////////////
// GetInteger()
//
// Attempts to parse an integer from the character string given.
// Returns true only if no parsing error occurs.
/////////////////////////////////////////////////////////////////
bool GetInteger(char *data, int *val)
{
char *endPtr;
long int retVal;
assert(val);
errno = 0;
retVal = strtol(data, &endPtr, 0);
if (retVal == 0 && (errno != 0 || data == endPtr))
return false;
if (errno != 0 && (retVal == LONG_MAX || retVal == LONG_MIN))
return false;
if (retVal < (long) INT_MIN || retVal > (long) INT_MAX)
return false;
*val = (int) retVal;
return true;
}
/////////////////////////////////////////////////////////////////
// ReadParameters()
//
// Read initial distribution, transition, and emission
// parameters from a file.
/////////////////////////////////////////////////////////////////
void MSA::ReadParameters()
{
ifstream data;
emitPairs = VVF(256, VF(256, 1e-10));
emitSingle = VF(256, 1e-5);
// read initial state distribution and transition parameters
if (parametersInputFilename == string(""))
{
if (NumInsertStates == 1)
{
for (int i = 0; i < NumMatrixTypes; i++)
initDistrib[i] = initDistrib1Default[i];
for (int i = 0; i < 2 * NumInsertStates; i++)
gapOpen[i] = gapOpen1Default[i];
for (int i = 0; i < 2 * NumInsertStates; i++)
gapExtend[i] = gapExtend1Default[i];
}
else if (NumInsertStates == 2)
{
for (int i = 0; i < NumMatrixTypes; i++)
initDistrib[i] = initDistrib2Default[i];
for (int i = 0; i < 2 * NumInsertStates; i++)
gapOpen[i] = gapOpen2Default[i];
for (int i = 0; i < 2 * NumInsertStates; i++)
gapExtend[i] = gapExtend2Default[i];
}
else
{
cerr
<< "ERROR: No default initial distribution/parameter settings exist"
<< endl << " for " << NumInsertStates
<< " pairs of insert states. Use --paramfile." << endl;
exit(1);
}
alphabet = alphabetDefault;
for (int i = 0; i < (int) alphabet.length(); i++)
{
emitSingle[(unsigned char) tolower(alphabet[i])] =
emitSingleDefault[i];
emitSingle[(unsigned char) toupper(alphabet[i])] =
emitSingleDefault[i];
for (int j = 0; j <= i; j++)
{
emitPairs[(unsigned char) tolower(alphabet[i])][(unsigned char) tolower(
alphabet[j])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) tolower(alphabet[i])][(unsigned char) toupper(
alphabet[j])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) toupper(alphabet[i])][(unsigned char) tolower(
alphabet[j])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) toupper(alphabet[i])][(unsigned char) toupper(
alphabet[j])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) tolower(alphabet[j])][(unsigned char) tolower(
alphabet[i])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) tolower(alphabet[j])][(unsigned char) toupper(
alphabet[i])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) toupper(alphabet[j])][(unsigned char) tolower(
alphabet[i])] = emitPairsDefault[i][j];
emitPairs[(unsigned char) toupper(alphabet[j])][(unsigned char) toupper(
alphabet[i])] = emitPairsDefault[i][j];
}
}
}
else
{
data.open(parametersInputFilename.c_str());
if (data.fail())
{
cerr << "ERROR: Unable to read parameter file: "
<< parametersInputFilename << endl;
exit(1);
}
string line[3];
for (int i = 0; i < 3; i++)
{
if (!getline(data, line[i]))
{
cerr
<< "ERROR: Unable to read transition parameters from parameter file: "
<< parametersInputFilename << endl;
exit(1);
}
}
istringstream data2;
data2.clear();
data2.str(line[0]);
for (int i = 0; i < NumMatrixTypes; i++)
data2 >> initDistrib[i];
data2.clear();
data2.str(line[1]);
for (int i = 0; i < 2 * NumInsertStates; i++)
data2 >> gapOpen[i];
data2.clear();
data2.str(line[2]);
for (int i = 0; i < 2 * NumInsertStates; i++)
data2 >> gapExtend[i];
if (!getline(data, line[0]))
{
cerr << "ERROR: Unable to read alphabet from scoring matrix file: "
<< parametersInputFilename << endl;
exit(1);
}
// read alphabet as concatenation of all characters on alphabet line
alphabet = "";
string token;
data2.clear();
data2.str(line[0]);
while (data2 >> token)
alphabet += token;
for (int i = 0; i < (int) alphabet.size(); i++)
{
for (int j = 0; j <= i; j++)
{
float val;
data >> val;
emitPairs[(unsigned char) tolower(alphabet[i])][(unsigned char) tolower(
alphabet[j])] = val;
emitPairs[(unsigned char) tolower(alphabet[i])][(unsigned char) toupper(
alphabet[j])] = val;
emitPairs[(unsigned char) toupper(alphabet[i])][(unsigned char) tolower(
alphabet[j])] = val;
emitPairs[(unsigned char) toupper(alphabet[i])][(unsigned char) toupper(
alphabet[j])] = val;
emitPairs[(unsigned char) tolower(alphabet[j])][(unsigned char) tolower(
alphabet[i])] = val;
emitPairs[(unsigned char) tolower(alphabet[j])][(unsigned char) toupper(
alphabet[i])] = val;
emitPairs[(unsigned char) toupper(alphabet[j])][(unsigned char) tolower(
alphabet[i])] = val;
emitPairs[(unsigned char) toupper(alphabet[j])][(unsigned char) toupper(
alphabet[i])] = val;
}
}
for (int i = 0; i < (int) alphabet.size(); i++)
{
float val;
data >> val;
emitSingle[(unsigned char) tolower(alphabet[i])] = val;
emitSingle[(unsigned char) toupper(alphabet[i])] = val;
}
data.close();
}
}
/////////////////////////////////////////////////////////////////
// ParseParams()
//
// Parse all command-line options.
/////////////////////////////////////////////////////////////////
void MSA::printUsage()
{
cerr << "Usage:" << endl
<< " -G -- Get features for Classification on Posterior Probability Matrix." << endl
<< " -s1 <0|1|2> -- Use different Posterior Probability Matrix to do MSA." << endl
<< " -s2 <0|1> -- Use different Clustering Methods to generate Guide Tree." << endl
<< " -s3 <0|1> -- Use different Consistency Transformation Matrix." << endl << endl;
}
SafeVector<string> MSA::ParseParams(int argc, char **argv)
{
if (argc < 2)
{
printUsage();
exit(1);
}
SafeVector<string> sequenceNames;
int tempInt;
for (int i = 1; i < argc; i++)
{
if (argv[i][0] == '-')
{
//help
if (!strcmp(argv[i], "-help") || !strcmp(argv[i], "-?"))
{
printUsage();
exit(1);
//output file name
}
else if (!strcmp(argv[i], "-G")
|| !strcmp(argv[i], "-get"))
{
get_features = true;
}
else if (!strcmp(argv[i], "-s1")
|| !strcmp(argv[i], "-step1"))
{
if (!GetInteger(argv[++i], &tempInt) && i < argc - 1)
{
first_step = 3;
}
else
{
if(tempInt == 0)
{
first_step = 0;
}
else if(tempInt == 1)
{
first_step = 1;
}
else if(tempInt == 2)
{
first_step = 2;
}
else{
first_step = 3;
}
}
}
else if (!strcmp(argv[i], "-s2")
|| !strcmp(argv[i], "-step2"))
{
if (!GetInteger(argv[++i], &tempInt) && i < argc - 1)
{
second_step = 0;
}
else
{
if (tempInt > 1 || tempInt < 0)
{
second_step = 0;
}
else
{
if(tempInt == 0)
{
second_step = 0;
}
else
{
second_step = 1;
}
}
}
}
else if (!strcmp(argv[i], "-s3")
|| !strcmp(argv[i], "-step3"))
{
if (!GetInteger(argv[++i], &tempInt) && i < argc - 1)
{
third_step = 0;
}
else
{
if (tempInt > 1 || tempInt < 0)
{
third_step = 0;
}
else
{
if(tempInt == 0)
{
third_step = 0;
}
else
{
third_step = 1;
}
}
}
}
else if (!strcmp(argv[i], "-o") || !strcmp(argv[i], "--outfile"))
{
if (i < argc - 1)
{
alignOutFileName = argv[++i]; //get the file name
}
else
{
cerr << "ERROR: String expected for option " << argv[i]
<< endl;
exit(1);
}
// number of consistency transformations
}
// alignment order
else if (!strcmp(argv[i], "-a")
|| !strcmp(argv[i], "--alignment-order"))
{
enableAlignOrder = true;
}
// bad arguments
else
{
cerr << "ERROR: Unrecognized option: " << argv[i] << endl;
exit(1);
}
}
else
{
sequenceNames.push_back(string(argv[i]));
}
}
if (alignOutFileName.length() == 0)
{
alignOutFile = &std::cout;
}
else
{
cerr << "Open the output file " << alignOutFileName << endl;
alignOutFile = new ofstream(alignOutFileName.c_str(),
ios::binary | ios::out | ios::trunc);
}
return sequenceNames;
}
/////////////////////////////////////////////////////////////////
// ProcessTree()
//
// Process the tree recursively. Returns the aligned sequences
// corresponding to a node or leaf of the tree.
/////////////////////////////////////////////////////////////////
MultiSequence* MSA::ProcessTree(TreeNode *tree, MultiSequence *sequences,
const SafeVector<SafeVector<SparseMatrix *> > &sparseMatrices,
const ProbabilisticModel &model)
{
MultiSequence *result;
// check if this is a node of the alignment tree
//if (tree->GetSequenceLabel() == -1){
if (tree->leaf == NODE)
{
MultiSequence *alignLeft = ProcessTree(tree->left, sequences,
sparseMatrices, model);
MultiSequence *alignRight = ProcessTree(tree->right, sequences,
sparseMatrices, model);
assert(alignLeft);
assert(alignRight);
result = AlignAlignments(alignLeft, alignRight, sparseMatrices, model);
assert(result);
delete alignLeft;
delete alignRight;
}
// otherwise, this is a leaf of the alignment tree
else
{
result = new MultiSequence();
assert(result);
result->AddSequence(sequences->GetSequence(tree->idx)->Clone());
}
return result;
}
/////////////////////////////////////////////////////////////////
// ComputeFinalAlignment()
//
// Compute the final alignment by calling ProcessTree(), then
// performing iterative refinement as needed.
/////////////////////////////////////////////////////////////////
MultiSequence* MSA::ComputeFinalAlignment(MSAGuideTree*tree,
MultiSequence *sequences,
const SafeVector<SafeVector<SparseMatrix *> > &sparseMatrices,
const ProbabilisticModel &model, int first_step)
{
MultiSequence *alignment = ProcessTree(tree->getRoot(), sequences,
sparseMatrices, model);
SafeVector<int> oldOrdering;
int numSeqs = alignment->GetNumSequences();
if (enableAlignOrder)
{
for (int i = 0; i < numSeqs; i++)
oldOrdering.push_back(alignment->GetSequence(i)->GetSortLabel());
alignment->SaveOrdering();
enableAlignOrder = false;
}
//DoIterativeRefinement() return 1,2: this refinement unsuccessful
if(first_step == 2 || numSeqs >= 150 ) numIterativeRefinementReps=10;
int ineffectiveness = 0;
for (int i = 0; i < numIterativeRefinementReps; i++)
{
int flag = DoIterativeRefinement(sparseMatrices, model, alignment);
if(numSeqs > 25 && numSeqs < 150 && first_step < 2)
{
if(flag > 0)
{
if(numIterativeRefinementReps < 4*numSeqs)
numIterativeRefinementReps ++;
if(flag == 1) ineffectiveness ++;
}
//else ineffectiveness = 0;
if(ineffectiveness > 2*numSeqs && i>100) break;
}
}
return alignment;
}
/////////////////////////////////////////////////////////////////
// AlignAlignments()
//
// Returns the alignment of two MultiSequence objects.
/////////////////////////////////////////////////////////////////
MultiSequence* MSA::AlignAlignments(MultiSequence *align1,
MultiSequence *align2,
const SafeVector<SafeVector<SparseMatrix *> > &sparseMatrices,
const ProbabilisticModel &model)
{
#if 0
VF *posterior = model.BuildPosterior (align1, align2, sparseMatrices, cutoff);
#else
VF *posterior = model.BuildPosterior(getSeqsWeights(), align1, align2,
sparseMatrices, cutoff);
#endif
// compute an "accuracy" measure for the MSA before refinement
pair<SafeVector<char> *, float> alignment;
//perform alignment
alignment = model.ComputeAlignment(align1->GetSequence(0)->GetLength(),
align2->GetSequence(0)->GetLength(), *posterior);
delete posterior;
// now build final alignment
MultiSequence *result = new MultiSequence();
for (int i = 0; i < align1->GetNumSequences(); i++)
result->AddSequence(
align1->GetSequence(i)->AddGaps(alignment.first, 'X'));
for (int i = 0; i < align2->GetNumSequences(); i++)
result->AddSequence(
align2->GetSequence(i)->AddGaps(alignment.first, 'Y'));
if (!enableAlignOrder)
result->SortByLabel();
// free temporary alignment
delete alignment.first;
return result;
}
/////////////////////////////////////////////////////////////////
// DoRelaxation()
//
// Performs one round of the weighted probabilistic consistency transformation.
/////////////////////////////////////////////////////////////////
SafeVector<SafeVector<SparseMatrix *> > MSA::DoRelaxation(float* seqsWeights,
MultiSequence *sequences,
SafeVector<SafeVector<SparseMatrix *> > &sparseMatrices)
{
const int numSeqs = sequences->GetNumSequences();
SafeVector<SafeVector<SparseMatrix *> > newSparseMatrices(numSeqs,
SafeVector<SparseMatrix *>(numSeqs, NULL));
// for every pair of sequences
for (int i = 0; i < numSeqs; i++)
{
float wi = seqsWeights[i];
for (int j = i + 1; j < numSeqs; j++)
{
float wj = seqsWeights[j];
Sequence *seq1 = sequences->GetSequence(i);
Sequence *seq2 = sequences->GetSequence(j);
// get the original posterior matrix
VF *posteriorPtr = sparseMatrices[i][j]->GetPosterior();
assert(posteriorPtr);
VF &posterior = *posteriorPtr;
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
// contribution from the summation where z = x and z = y
float w = wi * wi * wj + wi * wj * wj;
float sumW = w;
for (int k = 0; k < (seq1Length + 1) * (seq2Length + 1); k++)
{
posterior[k] = w * posterior[k];
}
// contribution from all other sequences
for (int k = 0; k < numSeqs; k++)
{
if (k != i && k != j)
{
float wk = seqsWeights[k];
float w = wi * wj * wk;
sumW += w;
if (k < i)
Relax1(w, sparseMatrices[k][i], sparseMatrices[k][j],
posterior);
else if (k > i && k < j)
Relax(w, sparseMatrices[i][k], sparseMatrices[k][j],
posterior);
else
{
SparseMatrix *temp =
sparseMatrices[j][k]->ComputeTranspose();
Relax(w, sparseMatrices[i][k], temp, posterior);
delete temp;
}
}
}
//cerr<<"sumW "<<sumW<<endl;
for (int k = 0; k < (seq1Length + 1) * (seq2Length + 1); k++)
{
posterior[k] /= sumW;
}
// mask out positions not originally in the posterior matrix
SparseMatrix *matXY = sparseMatrices[i][j];
for (int y = 0; y <= seq2Length; y++)
posterior[y] = 0;
for (int x = 1; x <= seq1Length; x++)
{
SafeVector<PIF>::iterator XYptr = matXY->GetRowPtr(x);
SafeVector<PIF>::iterator XYend = XYptr + matXY->GetRowSize(x);
VF::iterator base = posterior.begin() + x * (seq2Length + 1);
int curr = 0;
while (XYptr != XYend)
{
// zero out all cells until the first filled column
while (curr < XYptr->first)
{
base[curr] = 0;
curr++;
}
// now, skip over this column
curr++;
++XYptr;
}
// zero out cells after last column
while (curr <= seq2Length)
{
base[curr] = 0;
curr++;
}
}
// save the new posterior matrix
newSparseMatrices[i][j] = new SparseMatrix(seq1->GetLength(),
seq2->GetLength(), posterior);
newSparseMatrices[j][i] = NULL;
delete posteriorPtr;
}
}
return newSparseMatrices;
}
/////////////////////////////////////////////////////////////////
// DoRelaxation()
//
// Performs one round of the unweighted probabilistic consistency transformation.
/////////////////////////////////////////////////////////////////
SafeVector<SafeVector<SparseMatrix *> > MSA::DoRelaxation(MultiSequence *sequences,
SafeVector<SafeVector<SparseMatrix *> > &sparseMatrices)
{
const int numSeqs = sequences->GetNumSequences();
SafeVector<SafeVector<SparseMatrix *> > newSparseMatrices(numSeqs,
SafeVector<SparseMatrix *>(numSeqs, NULL));
// for every pair of sequences
for (int i = 0; i < numSeqs; i++)
{
for (int j = i + 1; j < numSeqs; j++)
{
Sequence *seq1 = sequences->GetSequence(i);
Sequence *seq2 = sequences->GetSequence(j);
// get the original posterior matrix
VF *posteriorPtr = sparseMatrices[i][j]->GetPosterior();
assert(posteriorPtr);
VF &posterior = *posteriorPtr;
const int seq1Length = seq1->GetLength();
const int seq2Length = seq2->GetLength();
for (int k = 0; k < (seq1Length + 1) * (seq2Length + 1); k++)
{
posterior[k] += posterior[k];
}
// contribution from all other sequences
for (int k = 0; k < numSeqs; k++)
{
if (k != i && k != j)
{
if (k < i)
Relax1(sparseMatrices[k][i], sparseMatrices[k][j],posterior);
else if (k > i && k < j)
Relax(sparseMatrices[i][k], sparseMatrices[k][j],posterior);
else
{
SparseMatrix *temp = sparseMatrices[j][k]->ComputeTranspose();
Relax(sparseMatrices[i][k], temp, posterior);
delete temp;
}
}
}
for (int k = 0; k < (seq1Length + 1) * (seq2Length + 1); k++)
{