Examples with PosteriorSummary org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary used on opensource projects

Example 16 with PosteriorSummary

use of org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary in project gatk by broadinstitute.

the class AllelicSplitCallerModelStateUnitTest method testBasicInit.

@Test
public void testBasicInit() {
    final ACNVModeledSegment acnvModeledSegment = new ACNVModeledSegment(new SimpleInterval("1", 1000, 1500), new PosteriorSummary(-4000, -4001, -4002), new PosteriorSummary(-4000, -4001, -4002));
    final List<ACNVModeledSegment> tempList = new ArrayList<>();
    tempList.add(acnvModeledSegment);
    final AllelicBalanceCallerModelState state = AllelicBalanceCallerModelState.createInitialCNLOHCallerModelState(0.2, tempList, HomoSapiensConstants.DEFAULT_PLOIDY, CNLOHCaller.NUM_RHOS);
    Assert.assertNotNull(state);
    Assert.assertNotNull(state.getEffectivePis());
    Assert.assertTrue(state.getEffectivePis().length > 0);
    Assert.assertTrue(state.getmVals().length > 0);
    Assert.assertTrue(state.getnVals().length > 0);
    Assert.assertEquals(MathUtils.sum(state.getEffectivePis()), 1.0, 1e-10);
}

Example 17 with PosteriorSummary

use of org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary in project gatk-protected by broadinstitute.

the class CopyRatioModellerUnitTest method testRunMCMCOnCopyRatioSegmentedGenome.

/**
     * Tests Bayesian inference of the copy-ratio model via MCMC.
     * <p>
     *     Recovery of input values for the variance and outlier-probability global parameters is checked.
     *     In particular, the true input value of the variance must fall within
     *     {@link CopyRatioModellerUnitTest#MULTIPLES_OF_SD_THRESHOLD}
     *     standard deviations of the posterior mean and the standard deviation of the posterior must agree
     *     with the analytic value to within a relative error of
     *     {@link CopyRatioModellerUnitTest#RELATIVE_ERROR_THRESHOLD} for 250 samples
     *     (after 250 burn-in samples have been discarded).  Similar criteria are applied
     *     to the recovery of the true input value for the outlier probability.
     * </p>
     * <p>
     *     Furthermore, the number of truth values for the segment-level means falling outside confidence intervals of
     *     1-sigma, 2-sigma, and 3-sigma given by the posteriors in each segment should be roughly consistent with
     *     a normal distribution (i.e., ~32, ~5, and ~0, respectively; we allow for errors of
     *     {@link CopyRatioModellerUnitTest#DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_1_SIGMA},
     *     {@link CopyRatioModellerUnitTest#DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_2_SIGMA}, and
     *     {@link CopyRatioModellerUnitTest#DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_3_SIGMA}, respectively).
     *     The mean of the standard deviations of the posteriors for the segment-level means should also be
     *     recovered to within a relative error of {@link CopyRatioModellerUnitTest#RELATIVE_ERROR_THRESHOLD}.
     * </p>
     * <p>
     *     Finally, the recovered values for the latent outlier-indicator parameters should agree with those used to
     *     generate the data.  For each indicator, the recovered value (i.e., outlier or non-outlier) is taken to be
     *     that given by the majority of posterior samples.  We require that at least
     *     {@link CopyRatioModellerUnitTest#FRACTION_OF_OUTLIER_INDICATORS_CORRECT_THRESHOLD}
     *     of the 10000 indicators are recovered correctly.
     * </p>
     * <p>
     *     With these specifications, this unit test is not overly brittle (i.e., it should pass for a large majority
     *     of randomly generated data sets), but it is still brittle enough to check for correctness of the sampling
     *     (for example, specifying a sufficiently incorrect likelihood will cause the test to fail).
     * </p>
     */
@Test
public void testRunMCMCOnCopyRatioSegmentedGenome() throws IOException {
    final JavaSparkContext ctx = SparkContextFactory.getTestSparkContext();
    LoggingUtils.setLoggingLevel(Log.LogLevel.INFO);
    //load data (coverages and number of targets in each segment)
    final ReadCountCollection coverage = ReadCountCollectionUtils.parse(COVERAGES_FILE);
    //Genome with no SNPs
    final Genome genome = new Genome(coverage, Collections.emptyList());
    final SegmentedGenome segmentedGenome = new SegmentedGenome(SEGMENT_FILE, genome);
    //run MCMC
    final CopyRatioModeller modeller = new CopyRatioModeller(segmentedGenome);
    modeller.fitMCMC(NUM_SAMPLES, NUM_BURN_IN);
    //check statistics of global-parameter posterior samples (i.e., posterior mode and standard deviation)
    final Map<CopyRatioParameter, PosteriorSummary> globalParameterPosteriorSummaries = modeller.getGlobalParameterPosteriorSummaries(CREDIBLE_INTERVAL_ALPHA, ctx);
    final PosteriorSummary variancePosteriorSummary = globalParameterPosteriorSummaries.get(CopyRatioParameter.VARIANCE);
    final double variancePosteriorCenter = variancePosteriorSummary.getCenter();
    final double variancePosteriorStandardDeviation = (variancePosteriorSummary.getUpper() - variancePosteriorSummary.getLower()) / 2;
    Assert.assertEquals(Math.abs(variancePosteriorCenter - VARIANCE_TRUTH), 0., MULTIPLES_OF_SD_THRESHOLD * VARIANCE_POSTERIOR_STANDARD_DEVIATION_TRUTH);
    Assert.assertEquals(relativeError(variancePosteriorStandardDeviation, VARIANCE_POSTERIOR_STANDARD_DEVIATION_TRUTH), 0., RELATIVE_ERROR_THRESHOLD);
    final PosteriorSummary outlierProbabilityPosteriorSummary = globalParameterPosteriorSummaries.get(CopyRatioParameter.OUTLIER_PROBABILITY);
    final double outlierProbabilityPosteriorCenter = outlierProbabilityPosteriorSummary.getCenter();
    final double outlierProbabilityPosteriorStandardDeviation = (outlierProbabilityPosteriorSummary.getUpper() - outlierProbabilityPosteriorSummary.getLower()) / 2;
    Assert.assertEquals(Math.abs(outlierProbabilityPosteriorCenter - OUTLIER_PROBABILITY_TRUTH), 0., MULTIPLES_OF_SD_THRESHOLD * OUTLIER_PROBABILITY_POSTERIOR_STANDARD_DEVIATION_TRUTH);
    Assert.assertEquals(relativeError(outlierProbabilityPosteriorStandardDeviation, OUTLIER_PROBABILITY_POSTERIOR_STANDARD_DEVIATION_TRUTH), 0., RELATIVE_ERROR_THRESHOLD);
    //check statistics of segment-mean posterior samples (i.e., posterior means and standard deviations)
    final List<Double> meansTruth = loadList(MEANS_TRUTH_FILE, Double::parseDouble);
    int numMeansOutsideOneSigma = 0;
    int numMeansOutsideTwoSigma = 0;
    int numMeansOutsideThreeSigma = 0;
    final int numSegments = meansTruth.size();
    //segment-mean posteriors are expected to be Gaussian, so PosteriorSummary for
    // {@link CopyRatioModellerUnitTest#CREDIBLE_INTERVAL_ALPHA}=0.32 is
    //(posterior mean, posterior mean - posterior standard devation, posterior mean + posterior standard deviation)
    final List<PosteriorSummary> meanPosteriorSummaries = modeller.getSegmentMeansPosteriorSummaries(CREDIBLE_INTERVAL_ALPHA, ctx);
    final double[] meanPosteriorStandardDeviations = new double[numSegments];
    for (int segment = 0; segment < numSegments; segment++) {
        final double meanPosteriorCenter = meanPosteriorSummaries.get(segment).getCenter();
        final double meanPosteriorStandardDeviation = (meanPosteriorSummaries.get(segment).getUpper() - meanPosteriorSummaries.get(segment).getLower()) / 2.;
        meanPosteriorStandardDeviations[segment] = meanPosteriorStandardDeviation;
        final double absoluteDifferenceFromTruth = Math.abs(meanPosteriorCenter - meansTruth.get(segment));
        if (absoluteDifferenceFromTruth > meanPosteriorStandardDeviation) {
            numMeansOutsideOneSigma++;
        }
        if (absoluteDifferenceFromTruth > 2 * meanPosteriorStandardDeviation) {
            numMeansOutsideTwoSigma++;
        }
        if (absoluteDifferenceFromTruth > 3 * meanPosteriorStandardDeviation) {
            numMeansOutsideThreeSigma++;
        }
    }
    final double meanPosteriorStandardDeviationsMean = new Mean().evaluate(meanPosteriorStandardDeviations);
    Assert.assertEquals(numMeansOutsideOneSigma, 100 - 68, DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_1_SIGMA);
    Assert.assertEquals(numMeansOutsideTwoSigma, 100 - 95, DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_2_SIGMA);
    Assert.assertTrue(numMeansOutsideThreeSigma <= DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_3_SIGMA);
    Assert.assertEquals(relativeError(meanPosteriorStandardDeviationsMean, MEAN_POSTERIOR_STANDARD_DEVIATION_MEAN_TRUTH), 0., RELATIVE_ERROR_THRESHOLD);
    //check accuracy of latent outlier-indicator posterior samples
    final List<CopyRatioState.OutlierIndicators> outlierIndicatorSamples = modeller.getOutlierIndicatorsSamples();
    int numIndicatorsCorrect = 0;
    final int numIndicatorSamples = outlierIndicatorSamples.size();
    final List<Integer> outlierIndicatorsTruthAsInt = loadList(OUTLIER_INDICATORS_TRUTH_FILE, Integer::parseInt);
    final List<Boolean> outlierIndicatorsTruth = outlierIndicatorsTruthAsInt.stream().map(i -> i == 1).collect(Collectors.toList());
    for (int target = 0; target < coverage.targets().size(); target++) {
        int numSamplesOutliers = 0;
        for (final CopyRatioState.OutlierIndicators sample : outlierIndicatorSamples) {
            if (sample.get(target)) {
                numSamplesOutliers++;
            }
        }
        //take predicted state of indicator to be given by the majority of samples
        if ((numSamplesOutliers >= numIndicatorSamples / 2.) == outlierIndicatorsTruth.get(target)) {
            numIndicatorsCorrect++;
        }
    }
    final double fractionOfOutlierIndicatorsCorrect = (double) numIndicatorsCorrect / coverage.targets().size();
    Assert.assertTrue(fractionOfOutlierIndicatorsCorrect >= FRACTION_OF_OUTLIER_INDICATORS_CORRECT_THRESHOLD);
}

Example 18 with PosteriorSummary

use of org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary in project gatk-protected by broadinstitute.

the class AllelicSplitCallerModelStateUnitTest method testSerializationRoundTrip.

@Test
public void testSerializationRoundTrip() {
    final ACNVModeledSegment acnvModeledSegment = new ACNVModeledSegment(new SimpleInterval("1", 1000, 1500), new PosteriorSummary(-4000, -4001, -4002), new PosteriorSummary(-4000, -4001, -4002));
    final List<ACNVModeledSegment> tempList = new ArrayList<>();
    tempList.add(acnvModeledSegment);
    final AllelicBalanceCallerModelState state = AllelicBalanceCallerModelState.createInitialCNLOHCallerModelState(0.2, tempList, HomoSapiensConstants.DEFAULT_PLOIDY, CNLOHCaller.NUM_RHOS);
    SparkTestUtils.roundTripInKryo(state, AllelicBalanceCallerModelState.class, SparkContextFactory.getTestSparkContext().getConf());
}

Example 19 with PosteriorSummary

use of org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary in project gatk-protected by broadinstitute.

the class JointAFCRSegmenterUnitTest method testSegmentation.

@Test
public void testSegmentation() {
    final RandomGenerator rng = RandomGeneratorFactory.createRandomGenerator(new Random(563));
    // probability that a datum is a het i.e. #hets / (#hets + #targets)
    final double hetProportion = 0.25;
    final List<Double> trueWeights = Arrays.asList(0.2, 0.5, 0.3);
    final double[] trueMinorAlleleFractions = new double[] { 0.12, 0.32, 0.5 };
    final double[] trueLog2CopyRatios = new double[] { -2.0, 0.0, 1.7 };
    final List<AFCRHiddenState> trueJointStates = IntStream.range(0, trueLog2CopyRatios.length).mapToObj(n -> new AFCRHiddenState(trueMinorAlleleFractions[n], trueLog2CopyRatios[n])).collect(Collectors.toList());
    final double trueMemoryLength = 1e5;
    final double trueCauchyWidth = 0.2;
    final int initialNumCRStates = 20;
    final int initialNumAFStates = 20;
    final AlleleFractionGlobalParameters trueAFParams = new AlleleFractionGlobalParameters(1.0, 0.01, 0.01);
    final JointAFCRHMM trueJointModel = new JointAFCRHMM(trueJointStates, trueWeights, trueMemoryLength, trueAFParams, AllelicPanelOfNormals.EMPTY_PON, trueCauchyWidth);
    // generate joint truth
    final int chainLength = 10000;
    final List<SimpleInterval> positions = CopyRatioSegmenterUnitTest.randomPositions("chr1", chainLength, rng, trueMemoryLength / 4);
    final List<Integer> trueHiddenStates = trueJointModel.generateHiddenStateChain(positions);
    final List<AFCRHiddenState> trueAFCRSequence = trueHiddenStates.stream().map(trueJointModel::getHiddenStateValue).collect(Collectors.toList());
    final double[] trueCopyRatioSequence = trueAFCRSequence.stream().mapToDouble(AFCRHiddenState::getLog2CopyRatio).toArray();
    final double[] trueAlleleFractionSequence = trueAFCRSequence.stream().mapToDouble(AFCRHiddenState::getMinorAlleleFraction).toArray();
    // generate separate af and cr data
    final GammaDistribution biasGenerator = AlleleFractionSegmenterUnitTest.getGammaDistribution(trueAFParams, rng);
    final double outlierProbability = trueAFParams.getOutlierProbability();
    final AllelicCountCollection afData = new AllelicCountCollection();
    final List<Double> crData = new ArrayList<>();
    final List<Target> crTargets = new ArrayList<>();
    for (int n = 0; n < positions.size(); n++) {
        final SimpleInterval position = positions.get(n);
        final AFCRHiddenState jointState = trueAFCRSequence.get(n);
        final double minorFraction = jointState.getMinorAlleleFraction();
        final double log2CopyRatio = jointState.getLog2CopyRatio();
        if (rng.nextDouble() < hetProportion) {
            // het datum
            afData.add(AlleleFractionSegmenterUnitTest.generateAllelicCount(minorFraction, position, rng, biasGenerator, outlierProbability));
        } else {
            //target datum
            crTargets.add(new Target(position));
            crData.add(CopyRatioSegmenterUnitTest.generateData(trueCauchyWidth, log2CopyRatio, rng));
        }
    }
    final ReadCountCollection rcc = new ReadCountCollection(crTargets, Arrays.asList("SAMPLE"), new Array2DRowRealMatrix(crData.stream().mapToDouble(x -> x).toArray()));
    final JointAFCRSegmenter segmenter = JointAFCRSegmenter.createJointSegmenter(initialNumCRStates, rcc, initialNumAFStates, afData, AllelicPanelOfNormals.EMPTY_PON);
    final TargetCollection<SimpleInterval> tc = new HashedListTargetCollection<>(positions);
    final List<Pair<SimpleInterval, AFCRHiddenState>> segmentation = segmenter.findSegments();
    final List<ACNVModeledSegment> jointSegments = segmentation.stream().map(pair -> {
        final SimpleInterval position = pair.getLeft();
        final AFCRHiddenState jointState = pair.getRight();
        final PosteriorSummary crSummary = PerformJointSegmentation.errorlessPosterior(jointState.getLog2CopyRatio());
        final PosteriorSummary afSummary = PerformJointSegmentation.errorlessPosterior(jointState.getMinorAlleleFraction());
        return new ACNVModeledSegment(position, crSummary, afSummary);
    }).collect(Collectors.toList());
    final double[] segmentCopyRatios = jointSegments.stream().flatMap(s -> Collections.nCopies(tc.targetCount(s.getInterval()), s.getSegmentMeanPosteriorSummary().getCenter()).stream()).mapToDouble(x -> x).toArray();
    final double[] segmentMinorFractions = jointSegments.stream().flatMap(s -> Collections.nCopies(tc.targetCount(s.getInterval()), s.getMinorAlleleFractionPosteriorSummary().getCenter()).stream()).mapToDouble(x -> x).toArray();
    final double averageMinorFractionError = Arrays.stream(MathArrays.ebeSubtract(trueAlleleFractionSequence, segmentMinorFractions)).map(Math::abs).average().getAsDouble();
    final double averageCopyRatioError = Arrays.stream(MathArrays.ebeSubtract(trueCopyRatioSequence, segmentCopyRatios)).map(Math::abs).average().getAsDouble();
    Assert.assertEquals(averageMinorFractionError, 0, 0.04);
    Assert.assertEquals(averageCopyRatioError, 0, 0.04);
}

Example 20 with PosteriorSummary

use of org.broadinstitute.hellbender.utils.mcmc.PosteriorSummary in project gatk-protected by broadinstitute.

the class PerformJointSegmentation method errorlessPosterior.

@VisibleForTesting
protected static PosteriorSummary errorlessPosterior(final double value) {
    final PosteriorSummary result = new PosteriorSummary(value, value, value);
    result.setDeciles(new DecileCollection(Arrays.asList(value)));
    return result;
}