inline Vdouble SubstitutionProcessCollectionMember::getClassProbabilities() const
{
Vdouble vProb;
for (size_t i = 0; i < getRateDistribution()->getNumberOfCategories(); i++)
{
vProb.push_back(getRateDistribution()->getProbability(i));
}
return vProb;
}
void YNGKP_M8::updateMatrices()
{
AbstractBiblioSubstitutionModel::updateMatrices();
// homogeneization of the synonymous substittion rates
Vdouble vd;
for (unsigned int i = 0; i < pmixmodel_->getNumberOfModels(); i++)
{
vd.push_back(1 / pmixmodel_->getNModel(i)->Qij(synfrom_, synto_));
}
pmixmodel_->setVRates(vd);
}
void YNGKP_M3::updateMatrices()
{
for (unsigned int i = 0; i < lParPmodel_.size(); i++)
{
if (mapParNamesFromPmodel_.find(lParPmodel_[i].getName()) != mapParNamesFromPmodel_.end())
{
if (lParPmodel_[i].getName()[18] == 'V')
{
unsigned int ind = TextTools::toInt(lParPmodel_[i].getName().substr(19));
double x = getParameterValue("omega0");
for (unsigned j = 1; j < ind; j++)
{
x += getParameterValue("delta" + TextTools::toString(j));
}
lParPmodel_[i].setValue(x);
}
else
{
lParPmodel_[i].setValue(getParameter(getParameterNameWithoutNamespace(mapParNamesFromPmodel_[lParPmodel_[i].getName()])).getValue());
}
}
}
pmixmodel_->matchParametersValues(lParPmodel_);
// homogeneization of the synonymous substitution rates
Vdouble vd;
for (unsigned int i = 0; i < pmixmodel_->getNumberOfModels(); i++)
{
vd.push_back(1 / pmixmodel_->getNModel(i)->Qij(synfrom_, synto_));
}
pmixmodel_->setVRates(vd);
}
int main(int args, char** argv)
{
cout << "******************************************************************" << endl;
cout << "* Bio++ Computation of site likelihoods inside mixed models *" << endl;
cout << "* Version 2.2.0. *" << endl;
cout << "* Author: L. Guéguen Last Modif.: 25/09/14 *" << endl;
cout << "******************************************************************" << endl;
cout << endl;
if (args == 1)
{
help();
return 0;
}
try
{
BppApplication bppmixedlikelihoods(args, argv, "BppMixedLikelihoods");
bppmixedlikelihoods.startTimer();
Alphabet* alphabet = SequenceApplicationTools::getAlphabet(bppmixedlikelihoods.getParams(), "", false);
auto_ptr<GeneticCode> gCode;
CodonAlphabet* codonAlphabet = dynamic_cast<CodonAlphabet*>(alphabet);
if (codonAlphabet) {
string codeDesc = ApplicationTools::getStringParameter("genetic_code", bppmixedlikelihoods.getParams(), "Standard", "", true, true);
ApplicationTools::displayResult("Genetic Code", codeDesc);
gCode.reset(SequenceApplicationTools::getGeneticCode(codonAlphabet->getNucleicAlphabet(), codeDesc));
}
// get the data
VectorSiteContainer* allSites = SequenceApplicationTools::getSiteContainer(alphabet, bppmixedlikelihoods.getParams());
VectorSiteContainer* sites = SequenceApplicationTools::getSitesToAnalyse(*allSites, bppmixedlikelihoods.getParams(), "", true, false);
delete allSites;
ApplicationTools::displayResult("Number of sequences", TextTools::toString(sites->getNumberOfSequences()));
ApplicationTools::displayResult("Number of sites", TextTools::toString(sites->getNumberOfSites()));
// Get the tree
Tree* tree = PhylogeneticsApplicationTools::getTree(bppmixedlikelihoods.getParams());
ApplicationTools::displayResult("Number of leaves", TextTools::toString(tree->getNumberOfLeaves()));
AbstractDiscreteRatesAcrossSitesTreeLikelihood* tl;
string nhOpt = ApplicationTools::getStringParameter("nonhomogeneous", bppmixedlikelihoods.getParams(), "no", "", true, false);
ApplicationTools::displayResult("Heterogeneous model", nhOpt);
MixedSubstitutionModel* model = 0;
MixedSubstitutionModelSet* modelSet = 0;
DiscreteDistribution* rDist = 0;
if (nhOpt == "no")
{
model = dynamic_cast<MixedSubstitutionModel*>(PhylogeneticsApplicationTools::getSubstitutionModel(alphabet, gCode.get(), sites, bppmixedlikelihoods.getParams()));
if (model == 0)
{
cout << "Model is not a Mixed model" << endl;
exit(0);
}
SiteContainerTools::changeGapsToUnknownCharacters(*sites);
if (model->getNumberOfStates() > model->getAlphabet()->getSize())
{
// Markov-modulated Markov model!
rDist = new ConstantRateDistribution();
}
else
{
rDist = PhylogeneticsApplicationTools::getRateDistribution(bppmixedlikelihoods.getParams());
}
tl = new RHomogeneousMixedTreeLikelihood(*tree, *sites, model, rDist, true);
}
else if (nhOpt == "one_per_branch")
{
model = dynamic_cast<MixedSubstitutionModel*>(PhylogeneticsApplicationTools::getSubstitutionModel(alphabet, gCode.get(), sites, bppmixedlikelihoods.getParams()));
if (model == 0)
{
cout << "Model is not a Mixed model" << endl;
exit(0);
}
SiteContainerTools::changeGapsToUnknownCharacters(*sites);
if (model->getNumberOfStates() > model->getAlphabet()->getSize())
{
// Markov-modulated Markov model!
rDist = new ConstantRateDistribution();
}
else
{
rDist = PhylogeneticsApplicationTools::getRateDistribution(bppmixedlikelihoods.getParams());
}
vector<double> rateFreqs;
if (model->getNumberOfStates() != alphabet->getSize())
{
// Markov-Modulated Markov Model...
unsigned int n = (unsigned int)(model->getNumberOfStates() / alphabet->getSize());
rateFreqs = vector<double>(n, 1. / (double)n); // Equal rates assumed for now, may be changed later (actually, in the most general case,
// we should assume a rate distribution for the root also!!!
}
std::map<std::string, std::string> aliasFreqNames;
FrequenciesSet* rootFreqs = PhylogeneticsApplicationTools::getRootFrequenciesSet(alphabet, gCode.get(), sites, bppmixedlikelihoods.getParams(), aliasFreqNames, rateFreqs);
vector<string> globalParameters = ApplicationTools::getVectorParameter<string>("nonhomogeneous_one_per_branch.shared_parameters", bppmixedlikelihoods.getParams(), ',', "");
modelSet = dynamic_cast<MixedSubstitutionModelSet*>(SubstitutionModelSetTools::createNonHomogeneousModelSet(model, rootFreqs, tree, aliasFreqNames, globalParameters));
model = 0;
tl = new RNonHomogeneousMixedTreeLikelihood(*tree, *sites, modelSet, rDist, true);
}
else if (nhOpt == "general")
{
modelSet = dynamic_cast<MixedSubstitutionModelSet*>(PhylogeneticsApplicationTools::getSubstitutionModelSet(alphabet, gCode.get(), sites, bppmixedlikelihoods.getParams()));
if (modelSet == 0)
{
cout << "Missing a Mixed model" << endl;
exit(0);
}
SiteContainerTools::changeGapsToUnknownCharacters(*sites);
if (modelSet->getNumberOfStates() > modelSet->getAlphabet()->getSize())
{
// Markov-modulated Markov model!
rDist = new ConstantDistribution(1.);
}
else
{
rDist = PhylogeneticsApplicationTools::getRateDistribution(bppmixedlikelihoods.getParams());
}
tl = new RNonHomogeneousMixedTreeLikelihood(*tree, *sites, modelSet, rDist, true);
}
else
throw Exception("Unknown option for nonhomogeneous: " + nhOpt);
tl->initialize();
double logL = tl->getValue();
if (isinf(logL))
{
// This may be due to null branch lengths, leading to null likelihood!
ApplicationTools::displayWarning("!!! Warning!!! Likelihood is zero.");
ApplicationTools::displayWarning("!!! This may be due to branch length == 0.");
ApplicationTools::displayWarning("!!! All null branch lengths will be set to 0.000001.");
ParameterList pl = tl->getBranchLengthsParameters();
for (unsigned int i = 0; i < pl.size(); i++)
{
if (pl[i].getValue() < 0.000001)
pl[i].setValue(0.000001);
}
tl->matchParametersValues(pl);
logL = tl->getValue();
}
if (isinf(logL))
{
ApplicationTools::displayError("!!! Unexpected likelihood == 0.");
ApplicationTools::displayError("!!! Looking at each site:");
for (unsigned int i = 0; i < sites->getNumberOfSites(); i++)
{
(*ApplicationTools::error << "Site " << sites->getSite(i).getPosition() << "\tlog likelihood = " << tl->getLogLikelihoodForASite(i)).endLine();
}
ApplicationTools::displayError("!!! 0 values (inf in log) may be due to computer overflow, particularily if datasets are big (>~500 sequences).");
exit(-1);
}
// Write parameters to screen:
ApplicationTools::displayResult("Log likelihood", TextTools::toString(tl->getValue(), 15));
ParameterList parameters = tl->getSubstitutionModelParameters();
for (unsigned int i = 0; i < parameters.size(); i++)
{
ApplicationTools::displayResult(parameters[i].getName(), TextTools::toString(parameters[i].getValue()));
}
parameters = tl->getRateDistributionParameters();
for (unsigned int i = 0; i < parameters.size(); i++)
{
ApplicationTools::displayResult(parameters[i].getName(), TextTools::toString(parameters[i].getValue()));
}
// /////////////////////////////////////////////
// Getting likelihoods per submodel
string outputFile;
outputFile = ApplicationTools::getAFilePath("output.likelihoods.file", bppmixedlikelihoods.getParams(), true, false);
ApplicationTools::displayResult("Output file for likelihoods", outputFile);
ofstream out(outputFile.c_str(), ios::out);
size_t nSites = sites->getNumberOfSites();
size_t nummodel = ApplicationTools::getParameter<size_t>("likelihoods.model_number", bppmixedlikelihoods.getParams(), 1, "", true, true);
string parname = ApplicationTools::getStringParameter("likelihoods.parameter_name", bppmixedlikelihoods.getParams(), "", "", true, false);
if (modelSet && ((nummodel <= 0) || (nummodel > modelSet->getNumberOfModels())))
{
ApplicationTools::displayError("Bad number of model " + TextTools::toString(nummodel) + ".");
exit(-1);
}
MixedSubstitutionModel* p0 = dynamic_cast<MixedSubstitutionModel*>(model ? model : modelSet->getModel(nummodel - 1));
if (!p0)
{
ApplicationTools::displayError("Model " + TextTools::toString(nummodel) + " is not a Mixed Model.");
exit(-1);
}
const AbstractBiblioMixedSubstitutionModel* ptmp = dynamic_cast<const AbstractBiblioMixedSubstitutionModel*>(p0);
if (ptmp) {
p0 = ptmp->getMixedModel().clone();
if (nhOpt == "no")
model = p0;
else {
modelSet->replaceModel(nummodel-1, p0);
modelSet->isFullySetUpFor(*tree);
}
}
//////////////////////////////////////////////////
// Case of a MixtureOfSubstitutionModels
MixtureOfSubstitutionModels* pMSM = dynamic_cast<MixtureOfSubstitutionModels*>(p0);
if (pMSM)
{
vector<string> colNames;
colNames.push_back("Sites");
size_t nummod = pMSM->getNumberOfModels();
for (unsigned int i = 0; i < nummod; i++)
{
colNames.push_back(pMSM->getNModel(i)->getName());
}
DataTable* rates = new DataTable(nSites, colNames.size());
rates->setColumnNames(colNames);
for (unsigned int i = 0; i < nSites; i++)
{
const Site* currentSite = &sites->getSite(i);
int currentSitePosition = currentSite->getPosition();
(*rates)(i, "Sites") = string("[" + TextTools::toString(currentSitePosition) + "]");
}
Vdouble vprob = pMSM->getProbabilities();
for (unsigned int i = 0; i < nummod; i++)
{
string modname = pMSM->getNModel(i)->getName();
for (unsigned int j = 0; j < nummod; j++)
{
pMSM->setNProbability(j, (j == i) ? 1 : 0);
}
if (tl)
delete tl;
if (nhOpt == "no")
tl = new RHomogeneousMixedTreeLikelihood(*tree, *sites, model, rDist, true, false, true);
else
tl = new RNonHomogeneousMixedTreeLikelihood(*tree, *sites, modelSet, rDist, false, true);
tl->initialize();
logL = tl->getValue();
Vdouble Vd = tl->getLogLikelihoodForEachSite();
for (unsigned int j = 0; j < nSites; j++)
{
(*rates)(j, modname) = TextTools::toString(Vd[j]);
}
ApplicationTools::displayMessage("\n");
ApplicationTools::displayMessage("Model " + modname + ":");
ApplicationTools::displayResult("Log likelihood", TextTools::toString(tl->getValue(), 15));
ApplicationTools::displayResult("Probability", TextTools::toString(vprob[i], 15));
}
DataTable::write(*rates, out, "\t");
}
//////////////////////////////////////////////////
// Case of a MixtureOfASubstitutionModel
else
{
MixtureOfASubstitutionModel* pMSM2 = dynamic_cast<MixtureOfASubstitutionModel*>(p0);
if (pMSM2 != NULL)
{
size_t nummod = pMSM2->getNumberOfModels();
if (parname == "")
{
ParameterList pl=pMSM2->getParameters();
for (size_t i2 = 0; i2 < pl.size(); i2++)
{
string pl2n = pl[i2].getName();
if (dynamic_cast<const ConstantDistribution*>(pMSM2->getDistribution(pl2n))==NULL)
{
parname=pl2n;
while (parname.size()>0 && pMSM2->getDistribution(parname)==NULL)
parname=pl2n.substr(0,pl2n.rfind("_"));
if (parname.size()>0){
ApplicationTools::displayResult("likelihoods.parameter_name", parname);
break;
}
}
}
}
if (parname == "")
{
ApplicationTools::displayError("Argument likelihoods.parameter_name is required.");
exit(-1);
}
vector< Vint > vvnmod;
size_t i2 = 0;
while (i2 < nummod)
{
string par2 = parname + "_" + TextTools::toString(i2 + 1);
Vint vnmod = pMSM2->getSubmodelNumbers(par2);
if (vnmod.size() == 0)
break;
vvnmod.push_back(vnmod);
i2++;
}
size_t nbcl = vvnmod.size();
if (nbcl==0)
throw Exception("Parameter " + parname + " is not mixed.");
Vdouble vprob = pMSM2->getProbabilities();
vector<vector<double> > vvprob;
vector<double> vsprob;
for (size_t i = 0; i < nbcl; i++)
{
vector<double> vprob2;
for (size_t j = 0; j < vvnmod[i].size(); j++)
{
vprob2.push_back(vprob[static_cast<size_t>(vvnmod[i][j])]);
}
vvprob.push_back(vprob2);
vsprob.push_back(VectorTools::sum(vvprob[i]));
}
vector<string> colNames;
colNames.push_back("Sites");
Vdouble dval;
for (unsigned int i = 0; i < nbcl; i++)
{
SubstitutionModel* pSM = pMSM2->getNModel(static_cast<size_t>(vvnmod[i][0]));
double valPar = pSM->getParameterValue(pSM->getParameterNameWithoutNamespace(parname));
dval.push_back(valPar);
colNames.push_back("Ll_" + parname + "=" + TextTools::toString(valPar));
}
for (unsigned int i = 0; i < nbcl; i++)
colNames.push_back("Pr_" + parname + "=" + TextTools::toString(dval[i]));
colNames.push_back("mean");
DataTable* rates = new DataTable(nSites, colNames.size());
rates->setColumnNames(colNames);
for (unsigned int i = 0; i < nSites; i++)
{
const Site* currentSite = &sites->getSite(i);
int currentSitePosition = currentSite->getPosition();
(*rates)(i,"Sites")=TextTools::toString(currentSitePosition);
}
VVdouble vvd;
vector<double> vRates = pMSM2->getVRates();
for (size_t i = 0; i < nbcl; ++i)
{
string par2 = parname + "_" + TextTools::toString(i + 1);
for (unsigned int j = 0; j < nummod; ++j)
pMSM2->setNProbability(j, 0);
for (size_t j = 0; j < vvprob[i].size(); ++j)
pMSM2->setNProbability(static_cast<size_t>(vvnmod[i][j]), vvprob[i][j] / vsprob[i]);
if (tl)
delete tl;
if (nhOpt == "no")
tl = new RHomogeneousMixedTreeLikelihood(*tree, *sites, model, rDist, true, false, true);
else
tl = new RNonHomogeneousMixedTreeLikelihood(*tree, *sites, modelSet, rDist, false, true);
tl->initialize();
logL = tl->getValue();
Vdouble vd = tl->getLogLikelihoodForEachSite();
for (unsigned int j = 0; j < nSites; j++)
(*rates)(j, i + 1) = TextTools::toString(vd[j]);
vvd.push_back(vd);
ApplicationTools::displayMessage("\n");
ApplicationTools::displayMessage("Parameter " + par2 + "=" + TextTools::toString(dval[i]) + " with rate=" + TextTools::toString(vRates[i]));
ApplicationTools::displayResult("Log likelihood", TextTools::toString(tl->getValue(), 15));
ApplicationTools::displayResult("Probability", TextTools::toString(vsprob[i], 15));
}
for (unsigned int j = 0; j < nSites; j++)
{
Vdouble vd;
for (unsigned int i = 0; i < nbcl; i++)
vd.push_back(std::log(vsprob[i])+vvd[i][j]);
VectorTools::logNorm(vd);
for (unsigned int i = 0; i < nbcl; i++)
(*rates)(j,nbcl + i + 1) = TextTools::toString(std::exp(vd[i]));
(*rates)(j, 2 * nbcl + 1) = TextTools::toString(VectorTools::sumExp(vd, dval));
}
DataTable::write(*rates, out, "\t");
}
}
delete alphabet;
delete sites;
if (model)
delete model;
if (modelSet)
delete modelSet;
delete rDist;
delete tl;
delete tree;
ApplicationTools::displayMessage("\n");
bppmixedlikelihoods.done();
}
catch (exception& e)
{
cout << e.what() << endl;
return 1;
}
return 0;
}
double simulate_with_dependence (string treeFile, double PI_1, double init_k, int total_positions, int num_pos_with_same_k, double k_increase, int is_gamma, double alpha, double beta, int num_cat)
{
//read Newick format tree
tree treeIn(treeFile);
//four states alphabet A C G T (will later be rplaced to 00,01,10,11)
alphabet* alph = new nucleotide;
sequenceContainer SC_all; //this will contain all positions
//parameters:
double PI_0 = 1-PI_1;
double k = init_k; //will be increased with each iteration
//parameters:
int jump_size = total_positions / num_pos_with_same_k;
for(int i=0; i<jump_size; i++)
{
Vdouble freqs; //stationary probabilities PI_00, PI_01, PI_10, PI_11
double TOTAL = k*PI_1*PI_1 + 2*PI_0*PI_1 + k*PI_0*PI_0;
freqs.push_back(k*PI_0*PI_0 / TOTAL); //PI_00 = k*PI_0*PI_0 / TOTAL
freqs.push_back(PI_0*PI_1 / TOTAL); //PI_01 = PI_0*PI_1 / TOTAL
freqs.push_back(PI_0*PI_1 / TOTAL); //PI_10 = PI_0*PI_1 / TOTAL
freqs.push_back(k*PI_1*PI_1 / TOTAL); //PI_11 = k*PI_1*PI_1 / TOTAL
//Q matrix (partial values - the rest are calculated by gtrModel using freqs and these values)
MDOUBLE a2c = PI_1; // --> c2a = freqs[a]*a2c/freqs[c] --> c2a = ((k*PI_0*PI_0 / TOTAL)*PI_1)/(PI_0*PI_1 / TOTAL) = k*PI_0
MDOUBLE a2g = PI_1;
MDOUBLE a2t = 0;
MDOUBLE c2g = 0;
MDOUBLE c2t = k*PI_1;
MDOUBLE g2t = k*PI_1;
//starting the evolutionary model
distribution *currDist = NULL;
if(is_gamma == 1)
{
currDist = new generalGammaDistribution(alpha,beta,num_cat); // ---> in the future we might want to turn these into param
}
else
{
currDist = new uniDistribution; // no among site rate variation
}
replacementModel *probMod = NULL;
pijAccelerator *pijAcc = NULL;
probMod = new gtrModel(freqs,a2c,a2g,a2t,c2g,c2t,g2t);
pijAcc = new trivialAccelerator(probMod);
stochasticProcess* _sp = new stochasticProcess(currDist, pijAcc);
//simulate:
simulateTree st1(treeIn, *_sp, alph);
st1.generate_seq(num_pos_with_same_k); //simulate num_pos_with_same_k positions with the current k
if(i == 0)
{
SC_all = st1.toSeqDataWithoutInternalNodes(); //first time
}
else
{
sequenceContainer SC = st1.toSeqDataWithoutInternalNodes(); //concatenate new positions to the ones you have
SC_all.concatenate(SC);
}
delete currDist;
delete probMod;
delete pijAcc;
delete _sp;
k = k + k_increase; //k = 1 , 1.05 , 1.1 , ... , 5.5
}
//prepare out file name:
std::stringstream sstm;
if(is_gamma == 1)
{
sstm << treeFile << ".gammaRateNoInv.PI_1=" << PI_1 << ".init_k=" << init_k << ".k_group_size=" << num_pos_with_same_k << ".k_increase=" << k_increase << ".fas";
}
else
{
sstm << treeFile << ".NoRate.PI_1=" << PI_1 << ".init_k=" << init_k << ".k_group_size=" << num_pos_with_same_k << ".k_increase=" << k_increase << ".fas";
}
std::string seqOutputFile = sstm.str();
//write out:
ofstream seq_sim(seqOutputFile.c_str());
fastaFormat::write(seq_sim,SC_all);
seq_sim.close();
delete alph;
return 0;
}