Examples with Histogram ubic.basecode.math.distribution.Histogram used on opensource projects

Example 1 with Histogram

use of ubic.basecode.math.distribution.Histogram in project Gemma by PavlidisLab.

the class DifferentialExpressionAnalyzerServiceImpl method addPvalueDistribution.

private void addPvalueDistribution(ExpressionAnalysisResultSet resultSet) {
    Histogram pvalHist = new Histogram("", 100, 0.0, 1.0);
    for (DifferentialExpressionAnalysisResult result : resultSet.getResults()) {
        Double pvalue = result.getPvalue();
        if (pvalue != null)
            pvalHist.fill(pvalue);
    }
    PvalueDistribution pvd = PvalueDistribution.Factory.newInstance();
    pvd.setNumBins(100);
    ByteArrayConverter bac = new ByteArrayConverter();
    pvd.setBinCounts(bac.doubleArrayToBytes(pvalHist.getArray()));
    // do not save yet.
    resultSet.setPvalueDistribution(pvd);
}

Example 2 with Histogram

use of ubic.basecode.math.distribution.Histogram in project Gemma by PavlidisLab.

the class ExpressionExperimentQCController method getDiffExPvalueHistXYSeries.

/**
 * @return JFreeChart XYSeries representing the histogram for the requested result set
 */
private XYSeries getDiffExPvalueHistXYSeries(ExpressionExperiment ee, Long analysisId, Long rsId, String factorName) {
    if (ee == null || analysisId == null || rsId == null) {
        log.warn("Got invalid values: " + ee + " " + analysisId + " " + rsId + " " + factorName);
        return null;
    }
    Histogram hist = differentialExpressionResultService.loadPvalueDistribution(rsId);
    XYSeries xySeries;
    if (hist != null) {
        xySeries = new XYSeries(rsId, true, true);
        Double[] binEdges = hist.getBinEdges();
        double[] counts = hist.getArray();
        assert binEdges.length == counts.length;
        for (int i = 0; i < binEdges.length; i++) {
            xySeries.add(binEdges[i].doubleValue(), counts[i]);
        }
        return xySeries;
    }
    return null;
}

Example 3 with Histogram

use of ubic.basecode.math.distribution.Histogram in project Gemma by PavlidisLab.

the class TwoChannelMissingValuesImpl method computeSignalThreshold.

/**
 * Determine a threshold based on the data.
 */
private Double computeSignalThreshold(ExpressionDataDoubleMatrix preferred, ExpressionDataDoubleMatrix signalChannelA, ExpressionDataDoubleMatrix signalChannelB, ExpressionDataDoubleMatrix baseChannel) {
    Double min = Double.MAX_VALUE;
    Double max = Double.MIN_VALUE;
    for (ExpressionDataMatrixRowElement element : baseChannel.getRowElements()) {
        CompositeSequence designElement = element.getDesignElement();
        int numCols = preferred.columns(designElement);
        for (int col = 0; col < numCols; col++) {
            Double[] signalA = null;
            if (signalChannelA != null) {
                signalA = signalChannelA.getRow(designElement);
            }
            Double[] signalB = null;
            if (signalChannelB != null) {
                signalB = signalChannelB.getRow(designElement);
            }
            Double sigAV = (signalA == null || signalA[col] == null) ? Double.NaN : signalA[col];
            Double sigBV = (signalB == null || signalB[col] == null) ? Double.NaN : signalB[col];
            if (!sigAV.isNaN() && sigAV < min) {
                min = sigAV;
            } else if (!sigBV.isNaN() && sigBV < min) {
                min = sigBV;
            } else if (!sigAV.isNaN() && sigAV > max) {
                max = sigAV;
            } else if (!sigBV.isNaN() && sigBV > max) {
                max = sigBV;
            }
        }
    }
    Histogram h = new Histogram("range", 100, min, max);
    for (ExpressionDataMatrixRowElement element : baseChannel.getRowElements()) {
        CompositeSequence designElement = element.getDesignElement();
        int numCols = preferred.columns(designElement);
        for (int col = 0; col < numCols; col++) {
            Double[] signalA = null;
            if (signalChannelA != null) {
                signalA = signalChannelA.getRow(designElement);
            }
            Double[] signalB = null;
            if (signalChannelB != null) {
                signalB = signalChannelB.getRow(designElement);
            }
            Double sigAV = (signalA == null || signalA[col] == null) ? Double.NaN : signalA[col];
            Double sigBV = (signalB == null || signalB[col] == null) ? Double.NaN : signalB[col];
            if (!sigAV.isNaN())
                h.fill(sigAV);
            if (!sigBV.isNaN())
                h.fill(sigBV);
        }
    }
    Double thresh = h.getApproximateQuantile(TwoChannelMissingValuesImpl.QUANTILE_OF_SIGNAL_TO_USE_IF_NO_BKG_AVAILABLE);
    TwoChannelMissingValuesImpl.log.info("Threshold based on signal=" + thresh);
    return thresh;
}

Example 4 with Histogram

use of ubic.basecode.math.distribution.Histogram in project Gemma by PavlidisLab.

the class ComBat method plot.

/**
 * Make diagnostic plots.
 * FIXME: As in the original ComBat, this only graphs the first batch's statistics. In principle we can (and perhaps
 * should) examine these plots for all the batches.
 *
 * @param filePrefix file prefix
 */
public void plot(String filePrefix) {
    if (this.gammaHat == null)
        throw new IllegalArgumentException("You must call 'run' first");
    /*
         * View the distribution of gammaHat, which we assume will have a normal distribution
         */
    DoubleMatrix1D ghr = gammaHat.viewRow(0);
    int NUM_HIST_BINS = 100;
    Histogram gammaHatHist = new Histogram("GammaHat", NUM_HIST_BINS, ghr);
    XYSeries ghplot = gammaHatHist.plot();
    Normal rn = new Normal(this.gammaBar.get(0), Math.sqrt(this.t2.get(0)), new MersenneTwister());
    Histogram ghtheoryT = new Histogram("Gamma", NUM_HIST_BINS, gammaHatHist.min(), gammaHatHist.max());
    for (int i = 0; i < 10000; i++) {
        double n = rn.nextDouble();
        ghtheoryT.fill(n);
    }
    XYSeries ghtheory = ghtheoryT.plot();
    File tmpfile;
    try {
        tmpfile = File.createTempFile(filePrefix + ".gammahat.histogram.", ".png");
        ComBat.log.info(tmpfile);
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    try (OutputStream os = new FileOutputStream(tmpfile)) {
        this.writePlot(os, ghplot, ghtheory);
        /*
             * View the distribution of deltaHat, which we assume has an inverse gamma distribution
             */
        DoubleMatrix1D dhr = deltaHat.viewRow(0);
        Histogram deltaHatHist = new Histogram("DeltaHat", NUM_HIST_BINS, dhr);
        XYSeries dhplot = deltaHatHist.plot();
        Gamma g = new Gamma(aPrior.get(0), bPrior.get(0), new MersenneTwister());
        Histogram deltaHatT = new Histogram("Delta", NUM_HIST_BINS, deltaHatHist.min(), deltaHatHist.max());
        for (int i = 0; i < 10000; i++) {
            double invg = 1.0 / g.nextDouble();
            deltaHatT.fill(invg);
        }
        XYSeries dhtheory = deltaHatT.plot();
        tmpfile = File.createTempFile(filePrefix + ".deltahat.histogram.", ".png");
        ComBat.log.info(tmpfile);
        try (OutputStream os2 = new FileOutputStream(tmpfile)) {
            this.writePlot(os2, dhplot, dhtheory);
        }
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

Example 5 with Histogram

use of ubic.basecode.math.distribution.Histogram in project Gemma by PavlidisLab.

the class DifferentialExpressionAnalyzerServiceTest method testWritePValuesHistogram.

@Test
public void testWritePValuesHistogram() {
    DifferentialExpressionAnalysisConfig config = new DifferentialExpressionAnalysisConfig();
    Collection<ExperimentalFactor> factors = ee.getExperimentalDesign().getExperimentalFactors();
    config.setFactorsToInclude(factors);
    Collection<DifferentialExpressionAnalysis> analyses = differentialExpressionAnalyzerService.runDifferentialExpressionAnalyses(ee, config);
    for (DifferentialExpressionAnalysis analysis : analyses) {
        differentialExpressionAnalysisService.thaw(analysis);
        for (ExpressionAnalysisResultSet resultSet : analysis.getResultSets()) {
            Histogram hist = resultService.loadPvalueDistribution(resultSet.getId());
            assertNotNull(hist);
        }
    }
}