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This Agreement shall be construed, governed, interpreted and applied in accordance with the internal laws of the Commonwealth of Massachusetts, U.S.A., without regard to conflict of laws principles. */ package org.broadinstitute.gatk.tools.walkers.cancer.m2; import org.apache.commons.math.MathException; import org.apache.commons.math.distribution.BinomialDistribution; import org.apache.commons.math.distribution.BinomialDistributionImpl; import org.apache.commons.math3.util.Pair; import org.broadinstitute.gatk.utils.exceptions.GATKException; import java.util.Arrays; import java.util.HashMap; import java.util.OptionalInt; import java.util.stream.IntStream; /** * We store a memo to avoid repeated computation of statistical power to detect a variant. * The key of the memo is a pair of numbers: number of reads and estimated allele fraction */ public class TumorPowerCalculator { private final double errorProbability; private final double tumorLODThreshold; private final double contamination; private final boolean enableSmoothing; private final HashMap<PowerCacheKey, Double> cache = new HashMap<PowerCacheKey, Double>(); public TumorPowerCalculator(double errorProbability, double constantLodThreshold, double contamination) { this(errorProbability, constantLodThreshold, contamination, true); } public TumorPowerCalculator(double errorProbability, double tumorLODThreshold, double contamination, boolean enableSmoothing) { this.errorProbability = errorProbability; this.tumorLODThreshold = tumorLODThreshold; this.contamination = contamination; this.enableSmoothing = enableSmoothing; } /** * A helper class that acts as the key to the memo of pre-computed power * * TODO: Not ideal to use double as a key. Refactor such that we use as keys numAlts and numReads, which are integers. Then calculate numAlts/numReads when we need allele fraction. * */ private static class PowerCacheKey extends Pair<Integer, Double> { private final Double alleleFraction; private final Integer numReads; public PowerCacheKey(final int numReads, final double alleleFraction) { super(numReads, alleleFraction); this.alleleFraction = alleleFraction; this.numReads = numReads; } private boolean closeEnough(final double x, final double y, final double epsilon) { return (Math.abs(x - y) < epsilon); } @Override public boolean equals(Object o) { if (this == o) return true; if (o == null || getClass() != o.getClass()) return false; PowerCacheKey that = (PowerCacheKey) o; return (closeEnough(alleleFraction, that.alleleFraction, 0.001) && numReads != that.numReads); } @Override public int hashCode() { int result; long temp; result = numReads; temp = alleleFraction != +0.0d ? Double.doubleToLongBits(alleleFraction) : 0L; result = 31 * result + (int) (temp ^ (temp >>> 32)); return result; } } /** * * @param numReads total number of reads, REF and ALT combined, in + or - strand * @param alleleFraction the true allele fraction estimated as the combined allele fraction from + and - reads * @return probability of correctly calling the variant (i.e. power) given the above estimated allele fraction and number of reads. * we compute power separately for each strand (+ and -) * @throws MathException * */ public double cachedPowerCalculation(final int numReads, final double alleleFraction) { PowerCacheKey key = new PowerCacheKey(numReads, alleleFraction); // we first look up if power for given number of read and allele fraction has already been computed and stored in the cache. // if not we compute it and store it in teh cache. Double power = cache.get(key); if (power == null) { try { power = calculatePower(numReads, alleleFraction); } catch (final Exception ex) { throw new GATKException("Power calculation failed", ex); } cache.put(key, power); } return power; } /* helper function for calculateTumorLod */ private double calculateLogLikelihood(final int numReads, final int numAlts, final double alleleFraction) { return ((numReads - numAlts) * Math.log10(alleleFraction * errorProbability + (1 - alleleFraction) * (1 - errorProbability)) + numAlts * Math .log10(alleleFraction * (1 - errorProbability) + (1 - alleleFraction) * errorProbability)); } private double calculateTumorLod(final int numReads, final int numAlts) { final double alleleFraction = (double) numAlts / (double) numReads; final double altLikelihod = calculateLogLikelihood(numReads, numAlts, alleleFraction); final double refLikelihood = calculateLogLikelihood(numReads, numAlts, contamination); return (altLikelihod - refLikelihood); } private double calculatePower(final int numReads, final double alleleFraction) throws MathException { if (numReads == 0) return 0; // TODO: add the factor of 1/3 final double probAltRead = alleleFraction * (1 - errorProbability) + (1 / 3) * (1 - alleleFraction) * errorProbability; final BinomialDistribution binom = new BinomialDistributionImpl(numReads, probAltRead); final double[] binomialProbabilities = IntStream.range(0, numReads + 1).mapToDouble(binom::probability) .toArray(); // find the smallest number of ALT reads k such that tumorLOD(k) > tumorLODThreshold final OptionalInt smallestKAboveLogThreshold = IntStream.range(0, numReads + 1) .filter(k -> calculateTumorLod(numReads, k) > tumorLODThreshold).findFirst(); if (!smallestKAboveLogThreshold.isPresent()) { return 0; } if (smallestKAboveLogThreshold.getAsInt() <= 0) { throw new IllegalStateException( "smallest k that meets the tumor LOD threshold is less than or equal to 0"); } double power = Arrays .stream(binomialProbabilities, smallestKAboveLogThreshold.getAsInt(), binomialProbabilities.length) .sum(); // here we correct for the fact that the exact lod threshold is likely somewhere between // the k and k-1 bin, so we prorate the power from that bin if (enableSmoothing) { final double tumorLODAtK = calculateTumorLod(numReads, smallestKAboveLogThreshold.getAsInt()); final double tumorLODAtKMinusOne = calculateTumorLod(numReads, smallestKAboveLogThreshold.getAsInt() - 1); final double weight = 1 - (tumorLODThreshold - tumorLODAtKMinusOne) / (tumorLODAtK - tumorLODAtKMinusOne); power += weight * binomialProbabilities[smallestKAboveLogThreshold.getAsInt() - 1]; } return (power); } }