Examples with Genome org.broadinstitute.hellbender.tools.exome.Genome used on opensource projects

Example 1 with Genome

use of org.broadinstitute.hellbender.tools.exome.Genome in project gatk by broadinstitute.

the class AlleleFractionModellerUnitTest method testBiasCorrection.

/**
     * Tests that the allelic PoN is appropriately used to correct reference bias.  The basic set up for the test data is
     * simulated hets at 1000 sites (1:1-1000) across 3 segments.  The outer two segments are balanced with
     * minor-allele fraction = 0.5; however, in the middle segment consisting of 100 sites (1:451-550), all of the sites
     *
     * <p>
     *     1) are balanced and have biases identical to the sites in the other two segments,
     *     which are drawn from a gamma distribution with alpha = 65, beta = 60 -> mean bias = 1.083 ("SAMPLE_NORMAL")
     * </p>
     *
     * <p>
     *     2) are balanced and have relatively high biases,
     *     which are drawn from a gamma distribution with alpha = 9, beta = 6 -> mean bias = 1.5 ("SAMPLE_WITH_BAD_SNPS")
     * </p>
     *
     * <p>
     *     3) have minor-allele fraction = 0.33, copy ratio = 1.5, and biases identical to the sites in the other two segments,
     *     which are drawn from a gamma distribution with alpha = 65, beta = 60 -> mean bias = 1.083 ("SAMPLE_EVENT").
     * </p>
     *
     * In this segment, using a PoN that doesn't know about the high reference bias of these sites ("ALLELIC_PON_NORMAL")
     * we should infer a minor-allele fraction of 6 / (6 + 9) = 0.40 in scenario 2; however, with a PoN that does know
     * about the high bias at these sites ("ALLELIC_PON_WITH_BAD_SNPS") we correctly infer that all of the segments are balanced.
     *
     * <p>
     *     Note that alpha and beta are not actually correctly recovered in this PoN via MLE because the biases are
     *     drawn from a mixture of gamma distributions (as opposed to a single gamma distribution as assumed in the model).
     *     TODO https://github.com/broadinstitute/gatk-protected/issues/421
     * </p>
     */
@Test(dataProvider = "biasCorrection")
public void testBiasCorrection(final AllelicCountCollection sample, final AllelicPanelOfNormals allelicPoN, final double minorFractionExpectedInMiddleSegment) {
    LoggingUtils.setLoggingLevel(Log.LogLevel.INFO);
    final JavaSparkContext ctx = SparkContextFactory.getTestSparkContext();
    final double minorFractionTolerance = 0.025;
    final Genome genome = new Genome(AlleleFractionSimulatedData.TRIVIAL_TARGETS, sample.getCounts());
    final List<SimpleInterval> segments = SegmentUtils.readIntervalsFromSegmentFile(SEGMENTS_FILE);
    final SegmentedGenome segmentedGenome = new SegmentedGenome(segments, genome);
    final int numSamples = 150;
    final int numBurnIn = 50;
    final AlleleFractionModeller modeller = new AlleleFractionModeller(segmentedGenome, allelicPoN);
    modeller.fitMCMC(numSamples, numBurnIn);
    final List<PosteriorSummary> minorAlleleFractionPosteriorSummaries = modeller.getMinorAlleleFractionsPosteriorSummaries(CREDIBLE_INTERVAL_ALPHA, ctx);
    final List<Double> minorFractionsResult = minorAlleleFractionPosteriorSummaries.stream().map(PosteriorSummary::getCenter).collect(Collectors.toList());
    final double minorFractionBalanced = 0.5;
    final List<Double> minorFractionsExpected = Arrays.asList(minorFractionBalanced, minorFractionExpectedInMiddleSegment, minorFractionBalanced);
    for (int segment = 0; segment < 3; segment++) {
        Assert.assertEquals(minorFractionsResult.get(segment), minorFractionsExpected.get(segment), minorFractionTolerance);
    }
}

Example 2 with Genome

use of org.broadinstitute.hellbender.tools.exome.Genome in project gatk by broadinstitute.

the class ACSModeledSegmentUtilsUnitTest method testConversion.

@Test
public void testConversion() {
    final List<ACNVModeledSegment> segs = SegmentUtils.readACNVModeledSegmentFile(new File(TEST_FILE_PATH));
    final Genome genome = new Genome(AlleleFractionSimulatedData.TRIVIAL_TARGETS, Collections.emptyList());
    final List<ACSModeledSegment> acsSegs = segs.stream().map(seg -> ACSModeledSegmentUtils.convertACNVSegmentToACSSegment(seg, 2.0, genome, true)).collect(Collectors.toList());
    for (int i = 0; i < segs.size(); i++) {
        Assert.assertEquals(acsSegs.get(i).getTau() / 2.0, segs.get(i).getSegmentMeanInCRSpace(), 1e-10);
    }
}

Example 3 with Genome

use of org.broadinstitute.hellbender.tools.exome.Genome in project gatk-protected by broadinstitute.

the class AlleleFractionModellerUnitTest method testBiasCorrection.

/**
     * Tests that the allelic PoN is appropriately used to correct reference bias.  The basic set up for the test data is
     * simulated hets at 1000 sites (1:1-1000) across 3 segments.  The outer two segments are balanced with
     * minor-allele fraction = 0.5; however, in the middle segment consisting of 100 sites (1:451-550), all of the sites
     *
     * <p>
     *     1) are balanced and have biases identical to the sites in the other two segments,
     *     which are drawn from a gamma distribution with alpha = 65, beta = 60 -> mean bias = 1.083 ("SAMPLE_NORMAL")
     * </p>
     *
     * <p>
     *     2) are balanced and have relatively high biases,
     *     which are drawn from a gamma distribution with alpha = 9, beta = 6 -> mean bias = 1.5 ("SAMPLE_WITH_BAD_SNPS")
     * </p>
     *
     * <p>
     *     3) have minor-allele fraction = 0.33, copy ratio = 1.5, and biases identical to the sites in the other two segments,
     *     which are drawn from a gamma distribution with alpha = 65, beta = 60 -> mean bias = 1.083 ("SAMPLE_EVENT").
     * </p>
     *
     * In this segment, using a PoN that doesn't know about the high reference bias of these sites ("ALLELIC_PON_NORMAL")
     * we should infer a minor-allele fraction of 6 / (6 + 9) = 0.40 in scenario 2; however, with a PoN that does know
     * about the high bias at these sites ("ALLELIC_PON_WITH_BAD_SNPS") we correctly infer that all of the segments are balanced.
     *
     * <p>
     *     Note that alpha and beta are not actually correctly recovered in this PoN via MLE because the biases are
     *     drawn from a mixture of gamma distributions (as opposed to a single gamma distribution as assumed in the model).
     *     TODO https://github.com/broadinstitute/gatk-protected/issues/421
     * </p>
     */
@Test(dataProvider = "biasCorrection")
public void testBiasCorrection(final AllelicCountCollection sample, final AllelicPanelOfNormals allelicPoN, final double minorFractionExpectedInMiddleSegment) {
    LoggingUtils.setLoggingLevel(Log.LogLevel.INFO);
    final JavaSparkContext ctx = SparkContextFactory.getTestSparkContext();
    final double minorFractionTolerance = 0.025;
    final Genome genome = new Genome(AlleleFractionSimulatedData.TRIVIAL_TARGETS, sample.getCounts());
    final List<SimpleInterval> segments = SegmentUtils.readIntervalsFromSegmentFile(SEGMENTS_FILE);
    final SegmentedGenome segmentedGenome = new SegmentedGenome(segments, genome);
    final int numSamples = 150;
    final int numBurnIn = 50;
    final AlleleFractionModeller modeller = new AlleleFractionModeller(segmentedGenome, allelicPoN);
    modeller.fitMCMC(numSamples, numBurnIn);
    final List<PosteriorSummary> minorAlleleFractionPosteriorSummaries = modeller.getMinorAlleleFractionsPosteriorSummaries(CREDIBLE_INTERVAL_ALPHA, ctx);
    final List<Double> minorFractionsResult = minorAlleleFractionPosteriorSummaries.stream().map(PosteriorSummary::getCenter).collect(Collectors.toList());
    final double minorFractionBalanced = 0.5;
    final List<Double> minorFractionsExpected = Arrays.asList(minorFractionBalanced, minorFractionExpectedInMiddleSegment, minorFractionBalanced);
    for (int segment = 0; segment < 3; segment++) {
        Assert.assertEquals(minorFractionsResult.get(segment), minorFractionsExpected.get(segment), minorFractionTolerance);
    }
}

Example 4 with Genome

use of org.broadinstitute.hellbender.tools.exome.Genome in project gatk 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 5 with Genome

use of org.broadinstitute.hellbender.tools.exome.Genome 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);
}