Examples with PCA uk.ac.babraham.SeqMonk.Analysis.Statistics.PCA used on opensource projects

Example 1 with PCA

use of uk.ac.babraham.SeqMonk.Analysis.Statistics.PCA in project SeqMonk by s-andrews.

the class HiCPCADomainQuantitation method run.

/* (non-Javadoc)
	 * @see java.lang.Runnable#run()
	 */
public void run() {
    // We're going to go through the probes one chromosome at a time so we
    // can reduce the complexity we have to deal with
    Chromosome[] chromosomes = application.dataCollection().genome().getAllChromosomes();
    for (int c = 0; c < chromosomes.length; c++) {
        if (cancel) {
            progressCancelled();
            return;
        }
        currentChromosome = chromosomes[c];
        Probe[] probes = application.dataCollection().probeSet().getProbesForChromosome(chromosomes[c]);
        if (probes.length < 5) {
            progressWarningReceived(new SeqMonkException("Too few probes on chromosome " + currentChromosome.name() + " - assigning zero to everything"));
            // It's not worth trying to find domains
            for (int d = 0; d < data.length; d++) {
                for (int p = 0; p < probes.length; p++) {
                    ((DataStore) data[d]).setValueForProbe(probes[p], 0f);
                }
            }
            continue;
        }
        ProbeList thisChrProbes = new ProbeList(application.dataCollection().probeSet(), chromosomes[c].name(), "", null);
        for (int p = 0; p < probes.length; p++) {
            thisChrProbes.addProbe(probes[p], 0f);
        }
        for (int d = 0; d < data.length; d++) {
            if (cancel) {
                progressCancelled();
                return;
            }
            currentStore = data[d];
            current = (d * chromosomes.length) + c;
            total = chromosomes.length * data.length;
            progressUpdated("Processing chromosome " + chromosomes[c].name() + " for " + data[d].name(), current, total);
            HeatmapMatrix matrix = new HeatmapMatrix(data[d], new ProbeList[] { thisChrProbes }, application.dataCollection().genome(), optionsPanel.minDistance(), optionsPanel.maxDistance(), optionsPanel.minStrength(), optionsPanel.maxSignificance(), optionsPanel.minAbsolute(), optionsPanel.correctLinkage());
            matrix.addProgressListener(this);
            wait = true;
            matrix.startCalculating();
            while (wait) {
                try {
                    Thread.sleep(100);
                } catch (InterruptedException e) {
                }
            }
            if (cancel) {
                progressCancelled();
                return;
            }
            if (matrix.filteredInteractions().length < 10) {
                progressWarningReceived(new SeqMonkException("Too few interactions on chromosome " + currentChromosome.name() + " for " + data[d].name() + " - assigning zero to everything"));
                // not going to get a sensible answer anyway.
                for (int p = 0; p < probes.length; p++) {
                    ((DataStore) data[d]).setValueForProbe(probes[p], 0f);
                }
                continue;
            }
            InteractionClusterMatrix clusterMatrix = new InteractionClusterMatrix(matrix.filteredInteractions(), probes.length);
            clusterMatrix.addListener(this);
            wait = true;
            clusterMatrix.startCorrelating();
            while (wait) {
                try {
                    Thread.sleep(100);
                } catch (InterruptedException e) {
                }
            }
            float[][] correlationMatrix = clusterMatrix.correlationMatix();
            // Annoyingly the PCA needs a double [][]
            double[][] correlationMatrixDouble = new double[correlationMatrix.length][];
            for (int i = 0; i < correlationMatrix.length; i++) {
                double[] db = new double[correlationMatrix[i].length];
                for (int j = 0; j < db.length; j++) {
                    db[j] = correlationMatrix[i][j];
                }
                correlationMatrixDouble[i] = db;
            }
            // Now we can calculate the PCA values from the correlation matrix
            PCA pca = new PCA(correlationMatrixDouble);
            pca.addProgressListener(this);
            wait = true;
            pca.startCalculating();
            while (wait) {
                try {
                    Thread.sleep(100);
                } catch (InterruptedException e) {
                }
            }
            double[] extractedEigenValues = pca.extractedEigenValues();
            // for these probes
            for (int p = 0; p < probes.length; p++) {
                ((DataStore) data[d]).setValueForProbe(probes[p], (float) extractedEigenValues[p]);
            }
        }
        thisChrProbes.delete();
    }
    quantitatonComplete();
}