final double standardDeviationQuality = qualityHistogram.getStandardDeviation();
final double standardDeviationQuality = qualityHistogram.getStandardDeviation();
@Test(dataProvider = "sumOfGaussiansDataProvider") public void testDrawSumOfQScores(final File metricsFile, final int altDepth, final double tolerance) throws Exception { final MetricsFile<TheoreticalSensitivityMetrics, Integer> metrics = new MetricsFile<>(); try (final FileReader metricsFileReader = new FileReader(metricsFile)) { metrics.read(metricsFileReader); } final List<Histogram<Integer>> histograms = metrics.getAllHistograms(); final Histogram<Integer> qualityHistogram = histograms.get(1); final TheoreticalSensitivity.RouletteWheel qualityRW = new TheoreticalSensitivity.RouletteWheel(TheoreticalSensitivity.trimDistribution(TheoreticalSensitivity.normalizeHistogram(qualityHistogram))); final Random randomNumberGenerator = new Random(51); // Calculate mean and deviation of quality score distribution to enable Gaussian sampling below final double averageQuality = qualityHistogram.getMean(); final double standardDeviationQuality = qualityHistogram.getStandardDeviation(); for (int k = 0; k < 1; k++) { int sumOfQualitiesFull = IntStream.range(0, altDepth).map(n -> qualityRW.draw()).sum(); int sumOfQualities = TheoreticalSensitivity.drawSumOfQScores(altDepth, averageQuality, standardDeviationQuality, randomNumberGenerator.nextGaussian()); Assert.assertEquals(sumOfQualitiesFull, sumOfQualities, sumOfQualitiesFull * tolerance); } }
@Test(dataProvider = "histogramData") public void testHistogramFunctions(final int[] values, final double mean, final double stdev, final Integer trimByWidth) { final Histogram<Integer> histo = new Histogram<>(); for (int value : values) { histo.increment(value); } if (trimByWidth != null) histo.trimByWidth(trimByWidth); final double m = histo.getMean(); final double sd = histo.getStandardDeviation(); Assert.assertEquals(round(mean), round(m), "Means are not equal"); Assert.assertEquals(round(stdev), round(sd), "Stdevs are not equal"); }
metrics.STANDARD_DEVIATION = trimmedHistogram.getStandardDeviation();
metrics.STANDARD_DEVIATION = trimmedHistogram.getStandardDeviation();
outieHistogram.trimByTailLimit(TAIL_LIMIT); metrics.JUMP_MEAN_INSERT_SIZE = outieHistogram.getMean(); metrics.JUMP_STDEV_INSERT_SIZE = outieHistogram.getStandardDeviation(); metrics.NONJUMP_PAIRS = innies; metrics.NONJUMP_DUPLICATE_PAIRS = innieDupes; innieHistogram.trimByTailLimit(TAIL_LIMIT); metrics.NONJUMP_MEAN_INSERT_SIZE = innieHistogram.getMean(); metrics.NONJUMP_STDEV_INSERT_SIZE = innieHistogram.getStandardDeviation(); metrics.CHIMERIC_PAIRS = crossChromPairs + superSized + tandemPairs; metrics.FRAGMENTS = fragments;
outieHistogram.trimByTailLimit(TAIL_LIMIT); metrics.JUMP_MEAN_INSERT_SIZE = outieHistogram.getMean(); metrics.JUMP_STDEV_INSERT_SIZE = outieHistogram.getStandardDeviation(); metrics.NONJUMP_PAIRS = innies; metrics.NONJUMP_DUPLICATE_PAIRS = innieDupes; innieHistogram.trimByTailLimit(TAIL_LIMIT); metrics.NONJUMP_MEAN_INSERT_SIZE = innieHistogram.getMean(); metrics.NONJUMP_STDEV_INSERT_SIZE = innieHistogram.getStandardDeviation(); metrics.CHIMERIC_PAIRS = crossChromPairs + superSized + tandemPairs; metrics.FRAGMENTS = fragments;
SD_COVERAGE = highQualityDepthHistogram.getStandardDeviation(); MEDIAN_COVERAGE = highQualityDepthHistogram.getMedian(); MAD_COVERAGE = highQualityDepthHistogram.getMedianAbsoluteDeviation();
SD_COVERAGE = highQualityDepthHistogram.getStandardDeviation(); MEDIAN_COVERAGE = highQualityDepthHistogram.getMedian(); MAD_COVERAGE = highQualityDepthHistogram.getMedianAbsoluteDeviation();