MultiTimeFieldAnalysis/r2bScore.cpp at main · BhallaLab/MultiTimeFieldAnalysis · GitHub

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#include <vector>

#include <random>

#include <numeric>

#include <pybind11/stl.h>

#include <pybind11/stl_bind.h>

#include <pybind11/pybind11.h>

#include <pybind11/numpy.h>

#include <Python.h>

#include <iostream>

using namespace std;

namespace py = pybind11;

#include "tcHeader.h"

PYBIND11_MAKE_OPAQUE(std::vector<double>);

//////////////////////////////////////////////////////////////////////////

// stuff for r2b calculations.

//////////////////////////////////////////////////////////////////////////

/**

* trialAve: Mean over ALL trials for each frame and cell.

* Returns the peak frame index for each cell.

* ret[cell][frame]

vector< unsigned int > trialAve( const double* data,

vector< vector< double > >& ret,

unsigned int numTrials, unsigned int numCells,

const AnalysisParams& ap )

{

unsigned int nf = ap.circShuffleFrames;

ret.resize( numCells );

for ( unsigned int cell = 0; cell < numCells; cell++ )

ret[cell].assign( nf, 0.0 );

vector< unsigned int > pk;

for ( unsigned int cell = 0; cell < numCells; cell++ ) {

vector< double >& mean = ret[cell];

for ( unsigned int ff = 0; ff < nf; ++ff ) {

const double* d = data + cell + (ff + ap.csOnsetFrame - ap.circPad) * numCells * numTrials;

for ( unsigned int tt = 0; tt < numTrials; tt++ )

mean[ff] += d[ tt * numCells ];

}

pk.push_back( max_element( mean.begin(), mean.end() ) - mean.begin() );

for ( unsigned int ff = 0; ff < nf; ++ff )

mean[ff] /= numTrials;

}

return pk;

}

/**

* shuffleTrialAve: Circularly shuffle ALL trials, return the mean and the

* peak frame of that shuffled mean. ret[cell][frame].

vector< unsigned int > shuffleTrialAve( const double* data,

vector< vector< double > >& ret,

unsigned int numTrials, unsigned int numCells,

std::uniform_int_distribution<std::mt19937::result_type>& shuffler,

std::mt19937& rng, const AnalysisParams& ap )

{

unsigned int nf = ap.circShuffleFrames;

ret.resize( numCells );

for ( auto& r : ret ) r.assign( nf, 0.0 );

unsigned int ncxnt = numCells * numTrials;

vector< unsigned int > shuff( numTrials );

for ( unsigned int tt = 0; tt < numTrials; ++tt )

shuff[tt] = shuffler( rng );

vector< unsigned int > pk;

for ( unsigned int cell = 0; cell < numCells; cell++ ) {

vector< double >& mean = ret[cell];

for ( unsigned int tt = 0; tt < numTrials; tt++ ) {

for ( unsigned int ff = 0; ff < nf; ++ff ) {

unsigned int circff = ap.csOnsetFrame - ap.circPad + (ff + shuff[tt]) % nf;

mean[ff] += data[ cell + circff * ncxnt + tt * numCells ];

}

pk.push_back( max_element( mean.begin(), mean.end() ) - mean.begin() );

for ( unsigned int ff = 0; ff < nf; ++ff )

mean[ff] /= numTrials;

}

return pk;

}

double r2b( const vector< double >& ave, unsigned int pkfr) {

unsigned int f0 = pkfr > 0 ? pkfr-1 : 0;

unsigned int f2 = pkfr < ave.size()-1 ? pkfr+1 : pkfr;

double ridge = ave[f0] + ave[pkfr] + ave[f2];

double background = std::accumulate( ave.begin(), ave.end(), 0.0 ) - ridge;

if ( ridge < 0.0 || background <= 0.0 ) {

return 0.0;

}

return ridge/background;

}

vector< CellScore > r2bScore( py::array_t<double> xs, const AnalysisParams& ap, double r2bThresh, double r2bPercentile)

{

unsigned int SEED = 1234;

py::buffer_info info = xs.request();

auto data = static_cast< double* >( info.ptr);

assert( info.shape[0] == ap.numFrames * ap.numCells * ap.numTrials );

std::mt19937 rng( SEED );

std::uniform_int_distribution<std::mt19937::result_type> shuffler(0, ap.circShuffleFrames);

vector< CellScore > ret( ap.numCells );

vector< vector< double > > tave;

trialAve( data, tave, ap.numTrials, ap.numCells, ap );

unsigned int minSepFrames = (unsigned int)round( ap.minPeakSep / ap.frameDt );

if ( minSepFrames < 1 ) minSepFrames = 1;

unsigned int nf = ap.circShuffleFrames;

// Peel candidate peaks from the full mean trace.

// Real R2B is also computed on the full mean.

vector< vector< unsigned int > > candidates( ap.numCells );

vector< vector< double > > realR2B( ap.numCells );

for ( unsigned int cc = 0; cc < ap.numCells; cc++ ) {

ret[cc].meanTrace = tave[cc];

vector< bool > available( nf, true );

while ( true ) {

unsigned int pkFrame = nf; // sentinel

double pkVal = -1e30;

for ( unsigned int ff = 0; ff < nf; ff++ )

if ( available[ff] && tave[cc][ff] > pkVal ) {

pkVal = tave[cc][ff]; pkFrame = ff;

}

if ( pkFrame == nf || pkVal <= 0.0 ) break;

candidates[cc].push_back( pkFrame );

realR2B[cc].push_back( r2b( tave[cc], pkFrame ) );

unsigned int lo = (pkFrame >= minSepFrames) ? pkFrame - minSepFrames : 0;

unsigned int hi = min( pkFrame + minSepFrames, nf - 1 );

for ( unsigned int ff = lo; ff <= hi; ff++ )

available[ff] = false;

}

// Bootstrap.

// Primary peak uses the max-statistic: shuffled data finds its own peak

// and we compare its R2B against the real primary-peak R2B.

// Secondary peaks use fixed-location tests on the shuffled mean.

vector< vector< double > > bootCounts( ap.numCells );

vector< double > sumShuff( ap.numCells, 0.0 );

for ( unsigned int cc = 0; cc < ap.numCells; cc++ )

bootCounts[cc].assign( candidates[cc].size(), 0.0 );

vector< vector< double > > shuffTave;

for ( unsigned int ii = 0; ii < ap.numShuffle; ++ii ) {

auto shuffPk = shuffleTrialAve( data, shuffTave, ap.numTrials, ap.numCells, shuffler, rng, ap );

for ( unsigned int cc = 0; cc < ap.numCells; cc++ ) {

if ( candidates[cc].empty() ) continue;

// Primary: max-statistic — shuffled R2B at shuffled peak.

double sr0 = r2b( shuffTave[cc], shuffPk[cc] );

if ( !isnan( sr0 ) ) {

sumShuff[cc] += sr0;

bootCounts[cc][0] += ( realR2B[cc][0] > sr0 );

}

// Secondary peaks: fixed location on shuffled mean.

for ( unsigned int pk = 1; pk < candidates[cc].size(); pk++ ) {

double sr = r2b( shuffTave[cc], candidates[cc][pk] );

if ( !isnan( sr ) )

bootCounts[cc][pk] += ( realR2B[cc][pk] > sr );

}

// Fill CellScore for each cell.

for ( unsigned int cc = 0; cc < ap.numCells; cc++ ) {

if ( candidates[cc].empty() ) continue;

ret[cc].meanPkIdx = candidates[cc][0];

ret[cc].baseScore = realR2B[cc][0];

ret[cc].meanScore = sumShuff[cc] / ap.numShuffle;

ret[cc].percentileScore = bootCounts[cc][0] / ap.numShuffle;

ret[cc].sigMean = ( ret[cc].baseScore > r2bThresh * ret[cc].meanScore );

ret[cc].sigBootstrap = ( ret[cc].percentileScore > r2bPercentile / 100.0 );

// Dip threshold: mean + dipSdev * sdev of this cell's mean trace.

double dipThresh;

{

double sum = 0.0, sumSq = 0.0;

for ( auto v : tave[cc] ) { sum += v; sumSq += v * v; }

double mn = sum / nf;

double sd = sqrt( sumSq / nf - mn * mn );

dipThresh = mn + ap.dipSdev * sd;

}

for ( unsigned int pk = 0; pk < candidates[cc].size(); pk++ ) {

double pctile = bootCounts[cc][pk] / ap.numShuffle;

if ( pctile <= r2bPercentile / 100.0 ) continue;

unsigned int pkFrame = candidates[cc][pk];

// Dip check: require dip between this peak and the nearest already-accepted peak.

if ( !ret[cc].allPkIndices.empty() ) {

unsigned int nearest = ret[cc].allPkIndices[0];

unsigned int minDist = (pkFrame > nearest) ? pkFrame - nearest : nearest - pkFrame;

for ( auto prevIdx : ret[cc].allPkIndices ) {

unsigned int d = (pkFrame > prevIdx) ? pkFrame - prevIdx : prevIdx - pkFrame;

if ( d < minDist ) { minDist = d; nearest = prevIdx; }

}

if ( !hasDipBetween( tave[cc], nearest, pkFrame, dipThresh, ap.dipFrames ) )

continue;

}

ret[cc].allPkIndices.push_back( pkFrame );

ret[cc].allPkScores.push_back( realR2B[cc][pk] );

ret[cc].allPkPvalues.push_back( 1.0 - pctile );

}

return ret;

}

MultiTimeFieldAnalysis/r2bScore.cpp at main · BhallaLab/MultiTimeFieldAnalysis · GitHub

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