use of org.broadinstitute.hellbender.tools.exome.allelefraction.AlleleFractionGlobalParameters in project gatk by broadinstitute.
the class AlleleFractionSegmenterUnitTest method testSegmentation.
@Test
public void testSegmentation() {
final RandomGenerator rng = RandomGeneratorFactory.createRandomGenerator(new Random(563));
final List<Double> trueWeights = Arrays.asList(0.2, 0.5, 0.3);
final List<Double> trueMinorAlleleFractions = Arrays.asList(0.12, 0.32, 0.5);
final double trueMemoryLength = 1e5;
final AlleleFractionGlobalParameters trueParams = new AlleleFractionGlobalParameters(1.0, 0.01, 0.01);
final AlleleFractionHMM trueModel = new AlleleFractionHMM(trueMinorAlleleFractions, trueWeights, trueMemoryLength, AllelicPanelOfNormals.EMPTY_PON, trueParams);
// randomly set positions
final int chainLength = 10000;
final List<SimpleInterval> positions = CopyRatioSegmenterUnitTest.randomPositions("chr1", chainLength, rng, trueMemoryLength / 4);
final List<Integer> trueStates = trueModel.generateHiddenStateChain(positions);
final List<Double> truthMinorFractions = trueStates.stream().map(trueModel::getMinorAlleleFraction).collect(Collectors.toList());
final AllelicCountCollection counts = generateCounts(truthMinorFractions, positions, rng, trueParams);
final AlleleFractionSegmenter segmenter = new AlleleFractionSegmenter(10, counts, AllelicPanelOfNormals.EMPTY_PON);
final List<ModeledSegment> segments = segmenter.getModeledSegments();
final double[] segmentMinorFractions = segments.stream().flatMap(s -> Collections.nCopies((int) s.getTargetCount(), s.getSegmentMean()).stream()).mapToDouble(x -> x).toArray();
final double averageMinorFractionError = IntStream.range(0, truthMinorFractions.size()).mapToDouble(n -> Math.abs(segmentMinorFractions[n] - truthMinorFractions.get(n))).average().getAsDouble();
Assert.assertEquals(averageMinorFractionError, 0, 0.01);
}
use of org.broadinstitute.hellbender.tools.exome.allelefraction.AlleleFractionGlobalParameters in project gatk 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);
}
use of org.broadinstitute.hellbender.tools.exome.allelefraction.AlleleFractionGlobalParameters in project gatk by broadinstitute.
the class AlleleFractionHMMUnitTest method constructorTest.
@Test
public void constructorTest() {
final double memoryLength = 1e6;
final List<Double> minorAlleleFractions = Arrays.asList(0.1, 0.5, 0.23);
final List<Double> weights = Arrays.asList(0.2, 0.2, 0.6);
final AlleleFractionGlobalParameters params = new AlleleFractionGlobalParameters(0.1, 0.01, 0.03);
final AlleleFractionHMM model = new AlleleFractionHMM(minorAlleleFractions, weights, memoryLength, AllelicPanelOfNormals.EMPTY_PON, params);
Assert.assertEquals(memoryLength, model.getMemoryLength());
for (int n = 0; n < weights.size(); n++) {
Assert.assertEquals(weights.get(n), model.getWeight(n));
Assert.assertEquals(minorAlleleFractions.get(n), model.getMinorAlleleFraction(n));
}
Assert.assertEquals(model.getParameters().getMeanBias(), params.getMeanBias());
Assert.assertEquals(model.getParameters().getBiasVariance(), params.getBiasVariance());
Assert.assertEquals(model.getParameters().getOutlierProbability(), params.getOutlierProbability());
}
use of org.broadinstitute.hellbender.tools.exome.allelefraction.AlleleFractionGlobalParameters in project gatk by broadinstitute.
the class AlleleFractionHMMUnitTest method equalMinorFractionsTest.
// if all states have the same minor fraction, then regardless of data the hidden state probabilities are
// proportional to the weights
@Test
public void equalMinorFractionsTest() {
// only the second state
final List<Double> weights = Arrays.asList(0.2, 0.3, 0.5);
final List<Double> minorAlleleFractions = Arrays.asList(0.3, 0.3, 0.3);
final double memoryLength = 1e3;
final AlleleFractionGlobalParameters params = new AlleleFractionGlobalParameters(0.1, 0.01, 0.03);
final AlleleFractionHMM model = new AlleleFractionHMM(minorAlleleFractions, weights, memoryLength, AllelicPanelOfNormals.EMPTY_PON, params);
final Random random = new Random(13);
final int chainLength = 10000;
final List<SimpleInterval> positions = new ArrayList<>();
final List<AllelicCount> data = new ArrayList<>();
int position = 1;
for (int n = 0; n < chainLength; n++) {
position += random.nextInt((int) (2 * memoryLength));
final SimpleInterval interval = new SimpleInterval("chr1", position, position);
positions.add(interval);
data.add(new AllelicCount(interval, random.nextInt(30) + 1, random.nextInt(30) + 1));
}
final ForwardBackwardAlgorithm.Result<AllelicCount, SimpleInterval, Integer> fbResult = ForwardBackwardAlgorithm.apply(data, positions, model);
for (int pos = 0; pos < chainLength; pos++) {
for (int state = 0; state < weights.size(); state++) {
Assert.assertEquals(fbResult.logProbability(pos, state), Math.log(weights.get(state)), 1e-5);
}
}
}
use of org.broadinstitute.hellbender.tools.exome.allelefraction.AlleleFractionGlobalParameters in project gatk-protected by broadinstitute.
the class AlleleFractionHMMUnitTest method equalMinorFractionsTest.
// if all states have the same minor fraction, then regardless of data the hidden state probabilities are
// proportional to the weights
@Test
public void equalMinorFractionsTest() {
// only the second state
final List<Double> weights = Arrays.asList(0.2, 0.3, 0.5);
final List<Double> minorAlleleFractions = Arrays.asList(0.3, 0.3, 0.3);
final double memoryLength = 1e3;
final AlleleFractionGlobalParameters params = new AlleleFractionGlobalParameters(0.1, 0.01, 0.03);
final AlleleFractionHMM model = new AlleleFractionHMM(minorAlleleFractions, weights, memoryLength, AllelicPanelOfNormals.EMPTY_PON, params);
final Random random = new Random(13);
final int chainLength = 10000;
final List<SimpleInterval> positions = new ArrayList<>();
final List<AllelicCount> data = new ArrayList<>();
int position = 1;
for (int n = 0; n < chainLength; n++) {
position += random.nextInt((int) (2 * memoryLength));
final SimpleInterval interval = new SimpleInterval("chr1", position, position);
positions.add(interval);
data.add(new AllelicCount(interval, random.nextInt(30) + 1, random.nextInt(30) + 1));
}
final ForwardBackwardAlgorithm.Result<AllelicCount, SimpleInterval, Integer> fbResult = ForwardBackwardAlgorithm.apply(data, positions, model);
for (int pos = 0; pos < chainLength; pos++) {
for (int state = 0; state < weights.size(); state++) {
Assert.assertEquals(fbResult.logProbability(pos, state), Math.log(weights.get(state)), 1e-5);
}
}
}
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