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/*
Copyright (C) 2011 Tal Pupko TalP@tauex.tau.ac.il.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "siteSpecificGL.h"
#include "definitions.h"
#include "numRec.h"
#include "matrixUtils.h"
#include "seqContainerTreeMap.h"
#include "gainLossUtils.h"
#include "gainLossModel.h"
#include "gainLossOptions.h"
// THE BAYESIAN EB_EXP PART OF gain and loss ESTIMATION. //
/*************************************
This function computes the expectation of
the posterior gain and loss distribution for a specific site
as well as the confidence interval
*************************************/
// per all sites computation
void computeEB_EXP_siteSpecificGL(Vdouble & GainLossV,
Vdouble & stdV,
Vdouble & lowerBoundV,
Vdouble & upperBoundV,
VVdouble & posteriorsV,
const sequenceContainer& sc,
const vector<vector<stochasticProcess*> >& spVVec,
const tree& tr,
const distribution * gainDist,
const distribution * lossDist,
const distribution * distPrim,
const MDOUBLE alphaConf,
VVVdouble & postProbPerSpPerCatPerPos, //2 fill (*postProbPerSpPerCatPerPos)[sp][pos]
unObservableData* unObservableData_p)
{
LOG(5,<<"Calculating posterior and expectation of posterior values for all sites"<<endl);
int seqLen = sc.seqLen();
GainLossV.resize(seqLen);
stdV.resize(seqLen);
lowerBoundV.resize(seqLen);
upperBoundV.resize(seqLen);
int numOfSPs = gainDist->categories()*lossDist->categories();
resizeMatrix(posteriorsV,seqLen,numOfSPs);
//computePijGam cpg;
//cpg._V.resize(numOfSPs);
//for (int i=0; i < numOfSPs; ++i) {
// int gainIndex =fromIndex2gainIndex(i,gainDist->categories(),lossDist->categories());
// int lossIndex =fromIndex2lossIndex(i,gainDist->categories(),lossDist->categories());
// cpg._V[i].fillPij(tr,*spVVec[gainIndex][lossIndex]);
//}
for (int pos=0; pos < sc.seqLen(); ++pos) {
computeEB_EXP_siteSpecificGL(pos, sc, spVVec, tr, gainDist,lossDist,distPrim,posteriorsV[pos], //cpg
GainLossV[pos], stdV[pos], lowerBoundV[pos], upperBoundV[pos], alphaConf, postProbPerSpPerCatPerPos,unObservableData_p);
}
}
/********************************************************************************************
*********************************************************************************************/
void computeEB_EXP_siteSpecificGL(int pos,
const sequenceContainer& sc,
const vector<vector<stochasticProcess*> >& spVVec,
//const computePijGam& cpg,
const tree &tr,
const distribution * gainDist,
const distribution * lossDist,
const distribution * distPrim,
Vdouble & posteriorV,
MDOUBLE& GainLossExpectation,
MDOUBLE & stdGainLoss,
MDOUBLE & lowerConf,
MDOUBLE & upperConf,
const MDOUBLE alphaConf,
VVVdouble & postProbPerSpPerCatPerPos, //2 fill (*postProbPerSpPerCatPerPos)[sp][pos]
unObservableData* unObservableData_p) // alpha of 0.05 is considered 0.025 for each side.
{
bool isLpostPerSpPerCatComputed =false;
if(postProbPerSpPerCatPerPos[0][0][pos]>0)
isLpostPerSpPerCatComputed =true;
// here we compute the posterior P(r|data)
int numOfRateCat = (*spVVec[0][0]).categories(); // ver2
int numOfSPs = gainDist->categories()*lossDist->categories();
posteriorV.resize(distPrim->categories(),0.0);
// ver2
VVdoubleRep PosteriorVVRateCat;
resizeMatrix(PosteriorVVRateCat,numOfSPs,numOfRateCat);
doubleRep dRepTotalLikelihood(0.0);// temporary dblRep for total likelihood
for (int spIndex=0; spIndex < numOfSPs; ++spIndex) {
int gainIndex =fromIndex2gainIndex(spIndex,gainDist->categories(),lossDist->categories());
int lossIndex =fromIndex2lossIndex(spIndex,gainDist->categories(),lossDist->categories());
//int primIndex;
//if(distPrim == gainDist)
// primIndex = gainIndex;
//else
// primIndex = lossIndex;
computePijGam pi;
pi.fillPij(tr,*spVVec[gainIndex][lossIndex]);
// ver1 - no rate dist in rate computation
//dblRepPosteriorV[primIndex] += likelihoodComputation::getLofPos(pos,tr,sc,pi,*spVVec[gainIndex][lossIndex])* gainDist->ratesProb(gainIndex)*lossDist->ratesProb(lossIndex);
// ver2 - with rate dist
for (int rateInd=0; rateInd < numOfRateCat; ++rateInd) {
PosteriorVVRateCat[spIndex][rateInd] += likelihoodComputation::getLofPos(pos,tr,sc,pi[rateInd],*spVVec[gainIndex][lossIndex],unObservableData_p)
* gainDist->ratesProb(gainIndex) * lossDist->ratesProb(lossIndex) * spVVec[gainIndex][lossIndex]->ratesProb(rateInd);
}
}
// here we compute sigma r * P(r | data)
GainLossExpectation = 0.0;
MDOUBLE sumOfSquares = 0.0; // this is the sum of squares. this will be used to compute the variance
// ver1 - no rate dist in rate computation
//for (int i=0; i < distPrim->categories(); ++i) {
// dblRepTotalLikelihood+=dblRepPosteriorV[i];
//}
//for (int j=0; j < distPrim->categories(); ++j) {
// dblRepPosteriorV[j]/=dblRepTotalLikelihood; // so that posteriorV is probability.
// if(unObservableData_p){
// dblRepPosteriorV[j] = dblRepPosteriorV[j]/(1- exp(unObservableData_p->getlogLforMissingData())); // Note: each postProbCat corrected by unObs of all cat
// }
// posteriorV[j] = convert(dblRepPosteriorV[j]); // revert back to DOUBLE
// MDOUBLE tmp = posteriorV[j]*distPrim->rates(j);
// GainLossExpectation += tmp;
// sumOfSquares += (tmp*distPrim->rates(j));
//}
// ver2
for (int spIndex=0; spIndex < numOfSPs; ++spIndex) {
for (int i=0; i < numOfRateCat; ++i) {
dRepTotalLikelihood+=PosteriorVVRateCat[spIndex][i];
}
}
for (int spIndex=0; spIndex < numOfSPs; ++spIndex) {
int gainIndex =fromIndex2gainIndex(spIndex,gainDist->categories(),lossDist->categories());
int lossIndex =fromIndex2lossIndex(spIndex,gainDist->categories(),lossDist->categories());
int primIndex;
if(distPrim == gainDist)
primIndex = gainIndex;
else
primIndex = lossIndex;
for (int i=0; i < numOfRateCat; ++i) {
PosteriorVVRateCat[spIndex][i]/=convert(dRepTotalLikelihood); // so that posteriorV is probability.
posteriorV[primIndex] += convert(PosteriorVVRateCat[spIndex][i]);
MDOUBLE tmp = convert(PosteriorVVRateCat[spIndex][i]) * distPrim->rates(primIndex) * spVVec[0][0]->rates(i); // the rateVal
GainLossExpectation += tmp;
sumOfSquares += (tmp * distPrim->rates(primIndex) * spVVec[0][0]->rates(i)); // ???
}
}
////////////////////////////////////////////////////////////////////////// ?
if(!isLpostPerSpPerCatComputed){
for (int spIndex=0; spIndex < numOfSPs; ++spIndex) {
for (int rateInd=0; rateInd < numOfRateCat; ++rateInd) {
postProbPerSpPerCatPerPos[spIndex][rateInd][pos] = convert(PosteriorVVRateCat[spIndex][rateInd]);
}
}
}
MDOUBLE variance = sumOfSquares - GainLossExpectation*GainLossExpectation; // variance
//if (!(variance!=0))
// errorMsg::reportError("Error in computeEB_EXP_siteSpecificGainLoss, variance = 0");
stdGainLoss = sqrt(variance); // standard deviation of inferred Ka/Ks
// detecting the confidence intervals.
MDOUBLE oneSideConfAlpha = alphaConf/2.0; // because we are computing the two tail.
MDOUBLE cdf = 0.0; // cumulative density function.
int k=0;
while (k < distPrim->categories()){
cdf += posteriorV[k];
if (cdf >oneSideConfAlpha) {
lowerConf = distPrim->rates(k);
break;
}
k++;
}
while (k < distPrim->categories()) {
if (cdf >(1.0-oneSideConfAlpha)) {
upperConf = distPrim->rates(k);
break;
}
++k;
cdf += posteriorV[k];
}
if (k==distPrim->categories())
upperConf = distPrim->rates(k-1);
}
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