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frequency_estimator.cpp
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frequency_estimator.cpp
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#include "frequency_estimator.h"
frequency_estimator::frequency_estimator(Eigen::MatrixXd* _pEigVecs, double _tol, double _maxLambda) :
skipIf(false), skipInfo(false), siteOnly(false), nelderMead(false), assumeHWD(false), gtError(0.005)
{
if ( _pEigVecs == NULL )
error("[E:%s:%d %s] Invalid eigenvectors", __FILE__, __LINE__, __PRETTY_FUNCTION__ );
pEigVecs = _pEigVecs;
nsamples = (int32_t)pEigVecs->rows();
ndims = (int32_t)pEigVecs->cols();
pSVD = new Eigen::BDCSVD<Eigen::MatrixXd>(*_pEigVecs, Eigen::ComputeThinU | Eigen::ComputeThinV);
// set dimensions;
tol = _tol;
maxLambda = _maxLambda;
hdr = NULL; wdr = NULL; iv = NULL;
hwe0z = hwe1z = ibc0 = ibc1 = 0;
pls = NULL; n_pls = 0; ploidies = NULL;
ifs = new float[nsamples];
betas = new float[ndims];
theta = 0;
pooled_af = -1;
isaf_computed = false;
gt = NULL;
gq = NULL;
}
frequency_estimator::~frequency_estimator() {
if ( ( pEigVecs ) && ( pSVD ) )
delete pSVD;
if (ifs != NULL )
delete [] ifs;
if ( gt != NULL )
free(gt);
if ( gq != NULL )
free(gq);
}
frequency_estimator::frequency_estimator(Eigen::BDCSVD<Eigen::MatrixXd>* _pSVD, double _tol, double _maxLambda) :
skipIf(false), skipInfo(false), siteOnly(false), gtError(0.005)
{
// set dimensions;
pEigVecs = NULL;
pSVD = _pSVD;
nsamples = (int32_t)pSVD->matrixU().rows();
ndims = (int32_t)pSVD->matrixU().cols();
tol = _tol;
maxLambda = _maxLambda;
hdr = NULL; wdr = NULL; iv = NULL;
hwe0z = hwe1z = ibc0 = ibc1 = 0;
pls = NULL; n_pls = 0; ploidies = NULL;
ifs = new float[nsamples];
betas = new float[ndims];
theta = 0;
pooled_af = -1;
isaf_computed = false;
}
bool frequency_estimator::set_hdr(bcf_hdr_t* _hdr, bcf_hdr_t* _wdr ) {
if ( hdr != _hdr ) {
hdr = _hdr;
wdr = _wdr;
char buffer[65535];
if ( !skipInfo ) {
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "HWE_SLP_P" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=HWE_SLP_P,Number=1,Type=Float,Description=\"Signed log p-value of HWE test with pooled allele frequencies\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "FIBC_P" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=FIBC_P,Number=1,Type=Float,Description=\"Inbreeding coefficient with pooled allele frequencies\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "HWE_SLP_I" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=HWE_SLP_I,Number=1,Type=Float,Description=\"Signed log p-value of HWE test with individual-sepcific allele frequencies\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "FIBC_I" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=FIBC_I,Number=1,Type=Float,Description=\"Inbreeding coefficient with individual-sepcific allele frequencies\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "MAX_IF" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=MAX_IF,Number=1,Type=Float,Description=\"Maximum Individual-specific allele frequency\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "MIN_IF" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=MIN_IF,Number=1,Type=Float,Description=\"Minimum Individual-specific allele frequency\">\n");
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "BETA_IF" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=BETA_IF,Number=%d,Type=Float,Description=\"Coefficients for intercept and each eigenvector to obtain ISAF\">\n", ndims);
bcf_hdr_append(wdr, buffer);
}
if ( bcf_hdr_id2int(_hdr, BCF_DT_ID, "LLK0" ) < 0 ) {
sprintf(buffer,"##INFO=<ID=LLK0,Number=1,Type=Float,Description=\"Null likelihood - for debug purpose\">\n");
bcf_hdr_append(wdr, buffer);
}
}
if ( ( !skipIf ) && ( !siteOnly ) ) {
sprintf(buffer,"##FORMAT=<ID=IF,Number=1,Type=Float,Description=\"Individual-specific allele frequencies\">\n");
bcf_hdr_append(wdr, buffer);
}
return ( bcf_hdr_sync(wdr) == 0 );
//return true;
}
else return false;
}
bool frequency_estimator::set_variant(bcf1_t* _iv, int8_t* _ploidies, int32_t* _pl) { //, std::vector<int32_t>* p_icols) {
iv = _iv;
ploidies = _ploidies;
if ( iv->n_sample != nsamples )
error("[E:%s:%d %s] nsamples %d != %d in the EigenVector", __FILE__, __LINE__, __PRETTY_FUNCTION__, iv->n_sample, nsamples);
//error("%d %d %d",bcf_hdr_nsamples(hdr),iv->n_sample,nsamples);
// parse PL fields
bcf_unpack(iv, BCF_UN_ALL);
if ( _pl != NULL ) { pls = _pl; n_pls = 3*nsamples; }
else {
bool plfound = false;
if ( field.empty() || ( field.compare("PL") == 0 ) ) {
if ( bcf_get_format_int32(hdr, iv, "PL", &pls, &n_pls) < 0 ) {
if ( field.compare("PL") == 0 )
error("[E:%s:%d %s] Cannot parse PL field", __FILE__, __LINE__, __PRETTY_FUNCTION__);
}
else
plfound = true;
}
if ( ( (!plfound) && field.empty() ) || field.compare("GL") == 0 ){
float* gls = NULL;
int32_t n_gls = 0;
if ( bcf_get_format_float(hdr, iv, "GL", &gls, &n_gls) < 0 ) {
error("[E:%s:%d %s] Cannot parse GL field", __FILE__, __LINE__, __PRETTY_FUNCTION__);
}
else {
if ( pls == NULL ) pls = new int32_t[n_gls];
for(int32_t i=0; i < nsamples; ++i) {
float maxgl = gls[3*i];
if ( gls[3*i+1] > maxgl ) maxgl = gls[3*i+1];
if ( gls[3*i+2] > maxgl ) maxgl = gls[3*i+2];
pls[3*i] = (int32_t)floor(-10*(gls[3*i]-maxgl)+0.5);
pls[3*i+1] = (int32_t)floor(-10*(gls[3*i+1]-maxgl)+0.5);
pls[3*i+2] = (int32_t)floor(-10*(gls[3*i+2]-maxgl)+0.5);
if ( pls[3*i] > 255 ) pls[3*i] = 255;
if ( pls[3*i+1] > 255 ) pls[3*i+1] = 255;
if ( pls[3*i+2] > 255 ) pls[3*i+2] = 255;
}
free(gls);
n_pls = n_gls;
}
}
else if ( field.compare("GT") == 0 ) {
int32_t* gts = NULL;
int32_t n_gts = 0;
double tmp = gtError + gtError*gtError;
int32_t errM[9] =
{ 0, (int32_t)floor(-10*log10(gtError/(1-tmp))+0.5), (int32_t)floor(-10*log10(gtError*gtError/(1-tmp))+0.5),
(int32_t)floor(-10*log10(tmp/(2-tmp-tmp))+0.5), 0, 0,
0, 0, 0 };
errM[5] = errM[3];
errM[6] = errM[2];
errM[7] = errM[1];
if ( bcf_get_genotypes(hdr, iv, >s, &n_gts) < 0 ) {
error("[E:%s:%d %s] Cannot parse GT field", __FILE__, __LINE__, __PRETTY_FUNCTION__);
}
else {
int32_t max_ploidy = n_gts/nsamples;
if ( max_ploidy != 2 )
error("[E:%s:%d %s] Multi-allelic (or Mono-allelic) variants found", __FILE__, __LINE__, __PRETTY_FUNCTION__);
if ( pls == NULL ) pls = new int32_t[nsamples*3];
for(int32_t i=0; i < nsamples; ++i) {
int32_t geno = bcf_gt_allele(gts[2*i])+bcf_gt_allele(gts[2*i+1]);
if ( bcf_gt_is_missing(gts[2*i+1]) ) { // haploid or missing
if ( bcf_gt_is_missing(gts[2*i]) ) { // missing
pls[3*i] = pls[3*i+1] = pls[3*i+2] = 0;
continue;
}
else { // pretend to be homozygous for haploid
geno = bcf_gt_allele(gts[2*i]) + bcf_gt_allele(gts[2*i]);
}
}
pls[3*i] = errM[geno*3];
pls[3*i+1] = errM[geno*3+1];
pls[3*i+2] = errM[geno*3+2];
}
free(gts);
n_pls = nsamples*3;
}
}
else {
if ( !plfound )
error("[E:%s:%d %s] Cannot recognize the field [%s]", __FILE__, __LINE__, __PRETTY_FUNCTION__, field.c_str());
}
}
/*
else if ( bcf_get_format_int32(hdr, iv, "PL", &pls, &n_pls) < 0 ) {
//float gls[nsamples+1];
float* gls = NULL;
int32_t n_gls = 0;
if ( bcf_get_format_float(hdr, iv, "GL", &gls, &n_gls) < 0 ) {
error("[E:%s:%d %s] Cannot parse PL or GL field", __FILE__, __LINE__, __PRETTY_FUNCTION__);
}
else {
if ( pls == NULL ) pls = new int32_t[n_gls];
for(int32_t i=0; i < nsamples; ++i) {
float maxgl = gls[3*i];
if ( gls[3*i+1] > maxgl ) maxgl = gls[3*i+1];
if ( gls[3*i+2] > maxgl ) maxgl = gls[3*i+2];
pls[3*i] = (int32_t)floor(-10*(gls[3*i]-maxgl)+0.5);
pls[3*i+1] = (int32_t)floor(-10*(gls[3*i+1]-maxgl)+0.5);
pls[3*i+2] = (int32_t)floor(-10*(gls[3*i+2]-maxgl)+0.5);
if ( pls[3*i] > 255 ) pls[3*i] = 255;
if ( pls[3*i+1] > 255 ) pls[3*i+1] = 255;
if ( pls[3*i+2] > 255 ) pls[3*i+2] = 255;
}
free(gls);
}
}
*/
pooled_af = -1;
isaf_computed = false;
return true;
//else return false;
}
double frequency_estimator::estimate_pooled_af_em(int32_t maxiter) {
if ( pooled_af < 0 ) {
double p = 0.5, q = 0.5;
double p0, p1, p2, sump, apsum, an;
for(int32_t i=0; i < maxiter; ++i) {
apsum = 0;
an = 0;
for(int32_t j=0; j < nsamples; ++j) {
if ( ploidies[j] == 2 ) {
p0 = q*q*phredConv.toProb(pls[j*3]);
p1 = 2*p*q*phredConv.toProb(pls[j*3+1]);
p2 = p*p*phredConv.toProb(pls[j*3+2]);
sump = p0+p1+p2;
apsum += (p1/sump + p2/sump*2.0);
an += 2;
}
else if ( ploidies[j] == 1 ) {
p0 = q*phredConv.toProb(pls[j*3]);
p2 = p*phredConv.toProb(pls[j*3+2]);
sump = p0+p2;
apsum += (p2/sump);
++an;
}
}
p = apsum/an;
q = 1.0-p;
}
pooled_af = p;
if ( pooled_af * nsamples < 0.5 ) { pooled_af = 0.5 / ( 1 + 2 *nsamples ); }
else if ( (1-pooled_af)*nsamples < 0.5 ) { pooled_af = 1 - 0.5/(1 + 2*nsamples); }
}
return pooled_af;
}
bool frequency_estimator::score_test_hwe(bool use_isaf) {
estimate_pooled_af_em();
double pp0 = (1.-pooled_af)*(1.-pooled_af);
double pp1 = 2*pooled_af*(1-pooled_af);
double pp2 = pooled_af*pooled_af;
double sumU0 = 0, sqU0 = 0, sumU1 = 0, sqU1 = 0;
double obsH0 = 0, obsH1 = 0, expH1 = 0;
double l0, l1, l2, sum1, sum0, ph1, ph0, U0, U1;
int32_t ndiploids = 0;
// pretend that we have pp0, pp1, pp2 observations of each genotype (pseudocount)
sumU0 = sumU1 = pp0*(1/pp0) + pp1 * (-2/pp1) + pp2*(1/pp2);
sqU0 = sqU1 = pp0*1/pp0/pp0 + pp1 * 4/pp1/pp1 + pp2*(1/pp2/pp2);
assumeHWD = false;
for(int32_t i=0; i < nsamples; ++i) {
if ( ploidies[i] != 2 ) continue;
++ndiploids;
l0 = phredConv.toProb(pls[i*3]);
l1 = phredConv.toProb(pls[i*3+1]);
l2 = phredConv.toProb(pls[i*3+2]);
sum0 = l0*pp0 + l1*pp1 + l2*pp2 + 1e-100;
ph0 = l1*pp1;
obsH0 += (ph0/sum0);
U0 = pooled_af*(1-pooled_af)*(l0-2*l1+l2)/sum0;
sumU0 += U0;
sqU0 += (U0*U0);
if ( use_isaf ) {
if ( nelderMead )
estimate_isaf_simplex();
else
estimate_isaf_em();
sum1 = l0*(1.-ifs[i])*(1.-ifs[i]) + 2*l1*(1.-ifs[i])*ifs[i] + l2*ifs[i]*ifs[i] + 1e-100;
ph1 = 2*l1*(1.-ifs[i])*ifs[i];
obsH1 += (ph1/sum1);
expH1 += (2*(1.-ifs[i])*ifs[i]);
U1 = (1-ifs[i])*ifs[i]*(l0-2*l1+l2)/sum1;
sumU1 += U1;
sqU1 += (U1*U1);
}
}
hwe0z = sumU0/sqrt(sqU0);
ibc0 = 1.0 - (obsH0+1)/(pp1*ndiploids+1);
if ( use_isaf ) {
hwe1z = sumU1/sqrt(sqU1);
ibc1 = 1.0 - (obsH1+1)/(expH1+1);
}
// temporary: calculate llk_null
Vector v(ndims+1);
v[0] = pooled_af * 2.0;
for(int32_t j=0; j < ndims; ++j)
v[j+1] = betas[j];
llknull = 0 - Evaluate(v);
return true;
}
/*
bool frequency_estimator::lr_test_hwe(bool use_isaf) {
estimate_isaf_em_hwd(); // estimate pooled allele frequency first
Vector v00(ndims+1); // null hypothesis : pooled AF with HWE
Vector v01(ndims+2); // alt1 hypohthesis : pooled AF with HWD (theta)
Vector v10(ndims+1); // alt2 hypothesis : is AF with HWE
Vector v11(ndims+2); // alt2 hypothesis : is AF with HWD (theta)
v00[0] = v01[0] = v10[0] = v11[0] = pooled_af * 2.0;
if ( theta < -1 ) { theta = -0.99999; }
else if ( theta > 1 ) { theta = 0.99999; }
for(int32_t i=0; i < ndims; ++i) {
v00[i] = v01[i] = 0;
v10[i] = v11[i] = betas[i];
}
double llk00, llk01, llk10, llk11;
assumeHWD = false;
llk00 = 0-Evaluate(v00);
llk10 = 0-Evaluate(v10);
v10[ndims+1] = v11[ndims+1] = 0.5 * (log(1.0+theta) - log(1.0-theta));
assumeHWD = true;
llk01 = 0-Evaluate(v01);
llk11 = 0-Evaluate(v11);
ibc0 = theta;
hwe0z = (llk01 < llk00) ? 0 : ( ( (ibc0 < 0) ? -1.0 : 1.0 ) * sqrt(2*(llk01 - llk00)) );
ibc1 = theta;
hwe1z = (llk11 < llk10) ? 0 : ( ( (ibc1 < 0) ? -1.0 : 1.0 ) * sqrt(2*(llk11 - llk10)) );
isaf_computed = true;
return true;
}
*/
double frequency_estimator::Evaluate(Vector& v) {
double llk = 0;
if ( ndims+1+(assumeHWD ? 1 : 0) != v.dim )
error("[E:%s:%d %s] Dimensions do not match %d vs %d", __FILE__, __LINE__, __PRETTY_FUNCTION__, ndims+1+(assumeHWD ? 1 : 0), v.dim);
double expGeno, isaf, isafQ, isafRR, isafRA, isafAA, offset, h;
if ( assumeHWD ) h = tanh(v[ndims+1]);
else h = 0;
for(int32_t i=0; i < nsamples; ++i) {
expGeno = v[0];
for(int32_t j=0; j < ndims; ++j)
expGeno += (pEigVecs->operator()(i,j) * v[j+1]);
isaf = expGeno/2.0;
if ( isaf*(nsamples+nsamples+1) < 0.5 ) isaf = 0.5/(nsamples+nsamples+1);
if ( (1.0-isaf)*(nsamples+nsamples+1) < 0.5 ) isaf = 1.0-0.5/(nsamples+nsamples+1);
isafQ = 1.0-isaf;
if ( assumeHWD ) {
offset = isaf * isafQ * h;
isafRR = isafQ * isafQ + offset;
isafRA = 2 * (1.0-h) * isaf * isafQ;
isafAA = isaf * isaf + offset;
if ( isafRR < 0.25/(nsamples+nsamples+1)/(nsamples+nsamples+1) ) {
isafRR = 0.25/(nsamples+nsamples+1)/(nsamples+nsamples+1);
offset = isafRR - isafQ * isafQ;
isafAA = isaf * isaf + offset;
isafRA = 2 * isaf * isafQ - 2 * offset;
}
else if ( isafAA < 0.25/(nsamples+nsamples+1)/(nsamples+nsamples+1) ) {
isafAA = 0.25/(nsamples+nsamples+1)/(nsamples+nsamples+1);
offset = isafAA - isaf * isaf;
isafRR = isafQ * isafQ + offset;
isafRA = 2 * isaf * isafQ - 2 * offset;
}
}
else {
isafRR = isafQ * isafQ;
isafRA = 2 * isaf * isafQ;
isafAA = isaf * isaf;
}
ifs[i] = isaf;
if ( ploidies[i] == 2 ) {
llk += log(isafRR * phredConv.toProb(pls[i*3]) +
isafRA * phredConv.toProb(pls[i*3+1]) +
isafAA * phredConv.toProb(pls[i*3+2]));
}
else if ( ploidies[i] == 1 ) {
llk += log(isafQ * phredConv.toProb(pls[i*3]) +
isaf * phredConv.toProb(pls[i*3+2]));
}
}
return 0-llk;
}
void frequency_estimator::estimate_isaf_em(int32_t maxiter) {
//Eigen::MatrixXd eV = Eigen::MatrixXd::Constant(nsamples,ndims+1,1.0);
//eV.block(0,1,nsamples,ndims) = *pEigVecs;
//Eigen::BDCSVD<Eigen::MatrixXd> svd(eV, Eigen::ComputeThinU | Eigen::ComputeThinV);
if ( !isaf_computed ) {
estimate_pooled_af_em();
Eigen::VectorXd y(nsamples);
Eigen::VectorXd isaf = Eigen::VectorXd::Constant(nsamples, pooled_af);
double maf = pooled_af > 0.5 ? 1-pooled_af : pooled_af;
//Eigen::VectorXd lambda = Eigen::VectorXd::Constant(ndims, maxLambda * (1.-maf) / (maf * nsamples * 2));
double lambda = maxLambda * (1.-maf) / (maf * nsamples * 2.0);
double p0, p1, p2;
for(int32_t i=0; i < maxiter; ++i) { // maxiter = 30
for(int32_t j=0; j < nsamples; ++j) {
if ( ploidies[j] == 2 ) {
p0 = ( 1.0 - isaf[j] ) * ( 1.0 - isaf[j] ) * phredConv.toProb(pls[3*j]);
p1 = 2.0 * isaf[j] * ( 1.0 - isaf[j] ) * phredConv.toProb(pls[3*j+1]);
p2 = isaf[j] * isaf[j] * phredConv.toProb(pls[3*j+2]);
y[j] = (p1+p2+p2+1e-100)/(p0+p1+p2+1e-100);
}
else if ( ploidies[j] == 1 ) {
p0 = ( 1.0 - isaf[j] ) * phredConv.toProb(pls[3*j]);
p2 = isaf[j] * phredConv.toProb(pls[3*j+2]);
y[j] = (p2+p2+1e-100)/(p0+p2+1e-100);
}
else {
y[j] = isaf[j] * 2;
}
}
// U diag(d_i^2/(d_i^2+lambda)) U'y
Eigen::VectorXd d2 = pSVD->singularValues();
Eigen::MatrixXd UD2 = pSVD->matrixU(); //
for(int32_t j=0; j < nsamples; ++j) {
for(int32_t k=0; k < ndims; ++k) {
//UD2(j,k) *= ( d2[k] / ( d2[k] + lambda ) );
UD2(j,k) *= ( d2[k] * d2[k] / ( d2[k] + lambda ) / ( d2[k] + lambda) );
}
}
isaf = UD2 * ( pSVD->matrixU().transpose() * y ) / 2.0;
for(int32_t j=0; j < nsamples; ++j) {
if ( isaf[j]*(nsamples+nsamples+1) < 0.5 ) isaf[j] = 0.5/(nsamples+nsamples+1.0);
else if ( (1.0-isaf[j])*(nsamples+nsamples+1) < 0.5 ) isaf[j] = 1.0-0.5/(nsamples+nsamples+1.0);
}
}
for(int32_t j=0; j < nsamples; ++j) {
ifs[j] = (float)isaf[j];
}
Eigen::VectorXd d2 = pSVD->singularValues();
Eigen::MatrixXd VD = pSVD->matrixV();
for(int32_t j=0; j < ndims; ++j) {
for(int32_t k=0; k < ndims; ++k) {
VD(j,k) *= ( d2[k] / ( d2[k] + lambda ) / ( d2[k] + lambda) );
}
}
Eigen::VectorXd vBeta = VD * ( pSVD->matrixU().transpose() * y ) / 2.0;
for(int32_t k=0; k < ndims; ++k)
betas[k] = (float)vBeta[k];
isaf_computed = true;
}
//return 0;
}
void frequency_estimator::estimate_isaf_em_hwd(int32_t maxiter) {
if ( !isaf_computed ) {
estimate_pooled_af_em();
Eigen::VectorXd yE(nsamples);
Eigen::VectorXd yD(nsamples);
Eigen::VectorXd isafE = Eigen::VectorXd::Constant(nsamples, pooled_af);
Eigen::VectorXd isafD = Eigen::VectorXd::Constant(nsamples, pooled_af);
double maf = pooled_af > 0.5 ? 1-pooled_af : pooled_af;
double lambda = maxLambda * (1.-maf) / (maf * nsamples * 2.0);
double p0E, p1E, p2E; // frequencies under HWE - priors
double p0D, p1D, p2D; // frequencies under HWD - priors
double x0E, x1E, x2E; // frequencies under HWE - posteriors
double x0D, x1D, x2D; // frequencies under HWD - posteriors
//notice("pooled_af = %lf, maf = %lf", pooled_af, maf);
double minAF = 0.5/(nsamples+nsamples+1.);
double minGF = minAF*minAF;
double thetaDouble = 0;
// U diag(d_i^2/(d_i^2+lambda)) U'y
Eigen::VectorXd d2 = pSVD->singularValues();
Eigen::MatrixXd UD2 = pSVD->matrixU(); //
for(int32_t j=0; j < nsamples; ++j) {
for(int32_t k=0; k < ndims; ++k) {
//UD2(j,k) *= ( d2[k] / ( d2[k] + lambda ) );
UD2(j,k) *= ( d2[k] * d2[k] / ( d2[k] + lambda ) / ( d2[k] + lambda) );
}
}
Eigen::MatrixXd VD = pSVD->matrixV();
for(int32_t j=0; j < ndims; ++j) {
for(int32_t k=0; k < ndims; ++k) {
VD(j,k) *= ( d2[k] / ( d2[k] + lambda ) / ( d2[k] + lambda) );
}
}
double l0, l1, l2;
for(int32_t i=0; i < maxiter; ++i) { // maxiter = 30
double thetaNum = 1e-100;
double thetaDen = 1e-100;
for(int32_t j=0; j < nsamples; ++j) {
if ( ploidies[j] == 2 ) {
// calculate isaf under HWD
p0E = ( 1.0 - isafD[j] ) * ( 1.0 - isafD[j] ); // * phredConv.toProb(pls[3*j]);
p1E = 2.0 * isafD[j] * ( 1.0 - isafD[j] ); // * phredConv.toProb(pls[3*j+1]);
p2E = isafD[j] * isafD[j]; // * phredConv.toProb(pls[3*j+2]);
p0D = p0E + 0.5 * thetaDouble * p1E;
p1D = p1E * (1.-thetaDouble);
p2D = p2E + 0.5 * thetaDouble * p1E;
if ( p0D < minGF ) { // boundary condition
p0D = minGF; // K = -p0E + minGF
p1D = p1E + 2.0*p0E - 2.0*minGF; // p1D = p1E - 2K = p1E + 2p0E - 2minGF
p2D = p2E - p0E + minGF; // p2D = p2E + K = p2E - p0E + minGF
}
else if ( p2D < minGF) {
p2D = minGF; // K = -p0E + minGF
p1D = p1E + 2.0*p2E - 2.0*minGF; // p1D = p1E - 2K = p1E + 2p0E - 2minGF
p0D = p0E - p2E + minGF; // p2D = p2E + K = p2E - p0E + minGF
}
// calculate isaf under HWE
p0E = ( 1.0 - isafE[j] ) * ( 1.0 - isafE[j] ); // * phredConv.toProb(pls[3*j]);
p1E = 2.0 * isafE[j] * ( 1.0 - isafE[j] ); // * phredConv.toProb(pls[3*j+1]);
p2E = isafE[j] * isafE[j]; // * phredConv.toProb(pls[3*j+2]);
l0 = phredConv.toProb(pls[3*j+0]);
l1 = phredConv.toProb(pls[3*j+1]);
l2 = phredConv.toProb(pls[3*j+2]);
x0E = p0E * l0;
x1E = p1E * l1;
x2E = p2E * l2;
x0D = p0D * l0;
x1D = p1D * l1;
x2D = p2D * l2;
yE[j] = (x1E+x2E+x2E+1e-100)/(x0E+x1E+x2E+1e-100);
yD[j] = (x1D+x2D+x2D+1e-100)/(x0D+x1D+x2D+1e-100);
//double w = fabs(l0 + l2 - l1 - l1); // heuristic for now.
thetaNum += (x1D / (x0D + x1D + x2D) );
thetaDen += (p1E);
}
else if ( ploidies[j] == 1 ) {
x0E = ( 1.0 - isafE[j] ) * phredConv.toProb(pls[3*j]);
x2E = isafE[j] * phredConv.toProb(pls[3*j+2]);
yE[j] = (x2E+x2E+1e-100)/(x0E+x2E+1e-100);
x0D = ( 1.0 - isafD[j] ) * phredConv.toProb(pls[3*j]);
x2D = isafD[j] * phredConv.toProb(pls[3*j+2]);
yD[j] = (x2D+x2D+1e-100)/(x0D+x2D+1e-100);
}
else { // this might be buggy for multi-allelics
yE[j] = isafE[j] * 2.0; // use expectation
yD[j] = isafD[j] * 2.0; // use expectation
}
}
// calculate isaf (which already reflects beta)
isafD = UD2 * ( pSVD->matrixU().transpose() * yD ) / 2.0;
isafE = UD2 * ( pSVD->matrixU().transpose() * yE ) / 2.0;
//notice("pooled_af = %lf, maf = %lf, minMAF = %lf", pooled_af, maf, minAF);
for(int32_t j=0; j < nsamples; ++j) {
if ( isafD[j] < minAF ) isafD[j] = minAF;
else if ( 1.0-isafD[j] < minAF ) isafD[j] = 1.0-minAF;
if ( isafE[j] < minAF ) isafE[j] = minAF;
else if ( 1.0-isafE[j] < minAF ) isafE[j] = 1.0-minAF;
}
thetaDouble = 1.0 - thetaNum/thetaDen;
}
for(int32_t j=0; j < nsamples; ++j) {
ifs[j] = (float)isafD[j];
}
Eigen::VectorXd vBetaD = VD * ( pSVD->matrixU().transpose() * yD ) / 2.0;
Eigen::VectorXd vBetaE = VD * ( pSVD->matrixU().transpose() * yE ) / 2.0;
for(int32_t k=0; k < ndims; ++k) {
betas[k] = (float)vBetaD[k];
//betasE[k] = (float)vBetaE[k];
}
theta = (float)thetaDouble;
// calculate likelihoods
double llk00 = 0, llk01 = 0, llk10 = 0, llk11 = 0;
double fE[3] = { (1.0-pooled_af)*(1.0-pooled_af),
2*pooled_af*(1.0-pooled_af),
pooled_af*pooled_af };
double fD[3] = { fE[0] + 0.5 * thetaDouble * fE[1],
fE[1] * (1.0-thetaDouble),
fE[2] + 0.5 * thetaDouble * fE[1] };
if ( fD[0] < minGF ) {
fD[0] = minGF;
fD[1] = fE[1] + 2*fE[0] - 2*minGF;
fD[2] = fE[2] - fE[0] + minGF;
}
else if ( fD[2] < minGF ) {
fD[2] = minGF;
fD[1] = fE[1] + 2*fE[2] - 2*minGF;
fD[0] = fE[0] - fE[2] + minGF;
}
for(int32_t j=0; j < nsamples; ++j) {
if ( ploidies[j] == 2 ) {
p0E = ( 1.0 - isafD[j] ) * ( 1.0 - isafD[j] ); // * phredConv.toProb(pls[3*j]);
p1E = 2.0 * isafD[j] * ( 1.0 - isafD[j] ); // * phredConv.toProb(pls[3*j+1]);
p2E = isafD[j] * isafD[j]; // * phredConv.toProb(pls[3*j+2]);
p0D = p0E + 0.5 * thetaDouble * p1E;
p1D = p1E * (1.0 - thetaDouble);
p2D = p2E + 0.5 * thetaDouble * p1E;
if ( p0D < minGF ) { // boundary condition
p0D = minGF; // K = -p0E + minGF
p1D = p1E + 2*p0E - 2*minGF; // p1D = p1E - 2K = p1E + 2p0E - 2minGF
p2D = p2E - p0E + minGF; // p2D = p2E + K = p2E - p0E + minGF
}
else if ( p2D < minGF ) {
p2D = minGF; // K = -p0E + minGF
p1D = p1E + 2*p2E - 2*minGF; // p1D = p1E - 2K = p1E + 2p0E - 2minGF
p0D = p0E - p2E + minGF; // p2D = p2E + K = p2E - p0E + minGF
}
p0E = ( 1.0 - isafE[j] ) * ( 1.0 - isafE[j] ); // * phredConv.toProb(pls[3*j]);
p1E = 2 * isafE[j] * ( 1.0 - isafE[j] ); // * phredConv.toProb(pls[3*j+1]);
p2E = isafE[j] * isafE[j]; // * phredConv.toProb(pls[3*j+2]);
l0 = phredConv.toProb(pls[3*j+0]);
l1 = phredConv.toProb(pls[3*j+1]);
l2 = phredConv.toProb(pls[3*j+2]);
llk00 += log(fE[0] * l0 + fE[1] * l1 + fE[2] * l2);
llk01 += log(fD[0] * l0 + fD[1] * l1 + fD[2] * l2);
llk10 += log(p0E * l0 + p1E * l1 + p2E * l2);
llk11 += log(p0D * l0 + p1D * l1 + p2D * l2);
}
else if ( ploidies[j] == 1 ) {
p0E = ( 1.0 - isafE[j] );
p2E = isafE[j];
p0D = ( 1.0 - isafD[j] );
p2D = isafD[j];
l0 = phredConv.toProb(pls[3*j+0]);
l2 = phredConv.toProb(pls[3*j+2]);
llk00 += log( (1.0-pooled_af) * l0 + pooled_af * l2);
llk01 += log( (1.0-pooled_af) * l0 + pooled_af * l2);
llk10 += log( p0E * l0 + p2E * l2);
llk11 += log( p0D * l0 + p2D * l2);
}
}
ibc0 = ibc1 = theta;
hwe0z = (llk01 < llk00) ? 0 : ( ( (ibc0 < 0) ? -1.0 : 1.0 ) * sqrt(2*(llk01 - llk00)) );
hwe1z = (llk11 < llk10) ? 0 : ( ( (ibc1 < 0) ? -1.0 : 1.0 ) * sqrt(2*(llk11 - llk10)) );
isaf_computed = true;
}
//return 0;
}
void frequency_estimator::estimate_isaf_simplex() {
if ( isaf_computed ) return;
assumeHWD = false;
AmoebaMinimizer isafMinimizer;
Vector startingPoint(ndims+1);
double emaf = estimate_pooled_af_em();
startingPoint.Zero();
startingPoint[0] = emaf*2.0;
isafMinimizer.func = this;
isafMinimizer.Reset(ndims+1);
isafMinimizer.point = startingPoint;
isafMinimizer.Minimize(tol);
Evaluate(isafMinimizer.point);
llknull = 0 - isafMinimizer.fmin;
//score_test_hwe(true, emaf);
isaf_computed = true;
//iter = isafMinimizer.cycleCount;
//return 0-isafMinimizer.fmin;
//return ancMinimizer.fmin;
}
void frequency_estimator::estimate_isaf_lrt() {
if ( isaf_computed ) return;
double emaf = estimate_pooled_af_em();
double llk0, llk1;
std::vector<double> p0(ndims+1);
std::vector<double> p1(ndims+1);
// Find MLE assuming HWE
{
assumeHWD = false;
AmoebaMinimizer isafMinimizer;
Vector startingPoint(ndims+1);
startingPoint.Zero();
startingPoint[0] = emaf*2.0;
isafMinimizer.func = this;
isafMinimizer.Reset(ndims+1);
isafMinimizer.point = startingPoint;
isafMinimizer.Minimize(tol);
Evaluate(isafMinimizer.point);
for(int i=0; i < ndims+1; ++i)
p0[i] = isafMinimizer.point[i];
llknull = llk0 = 0 - isafMinimizer.fmin;
//notice("ndims = %d, p0[0] = %.5lg, p0[1] = %.5lg, p0[2] = %.5lg, p0[3] = %.5lg", ndims, p0[0], p0[1], p0[2], p0[3]);
}
// Find MLE without assuming HWE
{
assumeHWD = true;
AmoebaMinimizer isafMinimizer;
Vector startingPoint(ndims+2);
startingPoint.Zero();
for(int i=0; i < ndims+1; ++i)
startingPoint[i] = p0[i];
isafMinimizer.func = this;
isafMinimizer.Reset(ndims+2);
isafMinimizer.point = startingPoint;
isafMinimizer.Minimize(tol);
Evaluate(isafMinimizer.point);
for(int i=0; i < ndims+1; ++i)
p1[i] = isafMinimizer.point[i];
theta = tanh(isafMinimizer.point[ndims+1]);
llk1 = 0 - isafMinimizer.fmin;
//notice("ndims = %d, p1[0] = %.5lg, p1[1] = %.5lg, p1[2] = %.5lg, p1[3] = %.5lg", ndims, p1[0], p1[1], p1[2], p1[3]);
}
double pp0 = (1.-emaf)*(1.-emaf);
double pp1 = 2 * emaf * (1-emaf);
double pp2 = emaf * emaf;
double obsH0 = 0, obsH1 = 0, expH1 = 0;
double l0, l1, l2, sumU0, sqU0, sum1, sum0, ph1, ph0, U0;
int32_t ndiploids = 0;
// pretend that we have pp0, pp1, pp2 observations of each genotype (pseudocount)
sumU0 = pp0*(1/pp0) + pp1 * (-2/pp1) + pp2*(1/pp2);
sqU0 = pp0*1/pp0/pp0 + pp1 * 4/pp1/pp1 + pp2*(1/pp2/pp2);
//int32_t ndiploids = 0;
for(int32_t i=0; i < nsamples; ++i) {
if ( ploidies[i] != 2 ) continue;
++ndiploids;
l0 = phredConv.toProb(pls[i*3]);
l1 = phredConv.toProb(pls[i*3+1]);
l2 = phredConv.toProb(pls[i*3+2]);
sum0 = l0*pp0 + l1*pp1 + l2*pp2 + 1e-100;
ph0 = l1*pp1;
obsH0 += (ph0/sum0);
U0 = emaf*(1-emaf)*(l0-2*l1+l2)/sum0;
sumU0 += U0;
sqU0 += (U0*U0);
sum1 = l0*(1.-ifs[i])*(1.-ifs[i]) + 2*l1*(1.-ifs[i])*ifs[i] + l2*ifs[i]*ifs[i] + 1e-100;
ph1 = 2*l1*(1.-ifs[i])*ifs[i];
obsH1 += (ph1/sum1);
expH1 += (2*(1.-ifs[i])*ifs[i]);
}
hwe0z = sumU0/sqrt(sqU0);
ibc0 = 1.0 - (obsH0+1)/(pp1*ndiploids+1);
//hwe1z = sumU1/sqrt(sqU1);
ibc1 = 1.0 - (obsH1+1)/(expH1+1);
hwe1z = (llk1 < llk0) ? 0 : ( ( (ibc1 < 0) ? -1.0 : 1.0 ) * sqrt(2*(llk1 - llk0)) );
isaf_computed = true;
}
bool frequency_estimator::update_gt_gq(bool update_gq) {
if ( siteOnly ) return false;
double gp = 0, gp_sum = 0, max_gp = 0;
int32_t best_gt = 0;
int32_t best_a1 = 0, best_a2 = 0;
int32_t an = 0;
int32_t acs[2] = {0,0};
int32_t gcs[3] = {0,0,0};
float afs[3];
int32_t max_gq = 0;
if ( gt == NULL )
gt = (int32_t*) malloc(sizeof(int32_t)*2*nsamples);
if ( ( update_gq ) && ( gq == NULL ) )
gq = (int32_t*) malloc(sizeof(int32_t)*nsamples);
for(int32_t i=0; i < nsamples; ++i) {
int32_t* pli = &pls[ i * 3 ];
if ( ploidies[i] == 1 ) {
max_gp = gp_sum = gp = ( phredConv.toProb((uint32_t)pli[0]) * (1.0 - ifs[i]) );
best_gt = 0; best_a1 = 0; best_a2 = 0;
gp = ( phredConv.toProb((uint32_t)pli[2]) * ifs[i] );
gp_sum += gp;
if ( max_gp < gp ) {
max_gp = gp;
best_gt = 2; best_a1 = 1; best_a2 = 1;
}
}
else if ( ploidies[i] == 2 ) {
max_gp = gp_sum = gp = ( phredConv.toProb((uint32_t)pli[0]) * (1.0-ifs[i]) * (1.0-ifs[i]) );
best_gt = 0; best_a1 = 0; best_a2 = 0;
gp = phredConv.toProb((uint32_t)pli[1]) * 2.0 * ifs[i] * (1.0-ifs[i]);
gp_sum += gp;
if ( max_gp < gp ) { max_gp = gp; best_gt = 1; best_a1 = 0; best_a2 = 1; }
gp = phredConv.toProb((uint32_t)pli[2]) * ifs[i] * ifs[i];
gp_sum += gp;
if ( max_gp < gp ) { max_gp = gp; best_gt = 2; best_a1 = 1; best_a2 = 1; }
}
else if ( ploidies[i] == 0 ) {
best_gt = 0;
max_gp = 0;
gp_sum = 1e-100;
}
else
error("[E:%s:%d %s] Unexpected ploidy %d", __FILE__, __LINE__, __PRETTY_FUNCTION__, (int32_t)ploidies[i]);
if ( update_gq ) {
double prob = 1.-max_gp/gp_sum; // to calculate GQ
if ( prob <= 3.162278e-26 )
prob = 3.162278e-26;
if ( prob > 1 )
prob = 1;
gq[i] = (int32_t)phredConv.err2Phred((double)prob);
if ( ( best_gt > 0 ) && ( max_gq < gq[i] ) ) {
max_gq = gq[i];
}
}
gt[2*i] = ((best_a1 + 1) << 1);
gt[2*i+1] = ((best_a2 + 1) << 1);
an += 2; // still use diploid representation of chrX for now.
++acs[best_a1];
++acs[best_a2];
++gcs[best_gt];
}
for(size_t i=0; i < 2; ++i) {
afs[i] = acs[i]/(float)an;
}
//notice("Calling bcf_update_format_int32() with nsamples=%d",nsamples);
bcf_update_format_int32(hdr, iv, "GT", gt, nsamples * 2);
if ( update_gq )
bcf_update_format_int32(hdr, iv, "GQ", gq, nsamples );
iv->qual = (float)max_gq;
bcf_update_info_int32(hdr, iv, "AC", &acs[1], 1);
bcf_update_info_int32(hdr, iv, "AN", &an, 1);
bcf_update_info_float(hdr, iv, "AF", &afs[1], 1);
bcf_update_info_int32(hdr, iv, "GC", gcs, 3);
bcf_update_info_int32(hdr, iv, "GN", &nsamples, 1);
return true;
}
bool frequency_estimator::update_variant() {
float hweslp0 = (float)((hwe0z > 0 ? -1 : 1) * log10( erfc(fabs(hwe0z)/sqrt(2.0)) + 1e-100 ));
float hweslp1 = (float)((hwe1z > 0 ? -1 : 1) * log10( erfc(fabs(hwe1z)/sqrt(2.0)) + 1e-100 ));
float max_if = 0, min_if = 1;
for(int32_t j=0; j < nsamples; ++j) {
if ( ifs[j] > max_if ) max_if = ifs[j];
if ( ifs[j] < min_if ) min_if = ifs[j];
}
if ( siteOnly ) {
//notice("foo");
bcf_subset(hdr, iv, 0, 0);
//notice("goo");
}
if ( !skipInfo ) {
float hweaf = (float)pooled_af;
bcf_update_info_float(wdr, iv, "HWEAF_P", &hweaf, 1);
bcf_update_info_float(wdr, iv, "FIBC_P", &ibc0, 1);
bcf_update_info_float(wdr, iv, "HWE_SLP_P", &hweslp0, 1);
bcf_update_info_float(wdr, iv, "FIBC_I", &ibc1, 1);
bcf_update_info_float(wdr, iv, "HWE_SLP_I", &hweslp1, 1);
bcf_update_info_float(wdr, iv, "MAX_IF", &max_if, 1);
bcf_update_info_float(wdr, iv, "MIN_IF", &min_if, 1);
bcf_update_info_float(wdr, iv, "LLK0", &llknull, 1);
bcf_update_info_float(wdr, iv, "BETA_IF", betas, ndims);
}
if ( ( !skipIf ) && ( !siteOnly ) ) {
bcf_update_format_float(wdr, iv, "IF", ifs, nsamples);
}
return true;
}