Examples with FloatVector uk.ac.babraham.SeqMonk.Utilities.FloatVector used on opensource projects

Example 1 with FloatVector

use of uk.ac.babraham.SeqMonk.Utilities.FloatVector in project SeqMonk by s-andrews.

the class PCADataCalculator method runPCA.

private void runPCA() {
    File tempDir;
    try {
        pd.progressUpdated("Creating temp directory", 0, 1);
        tempDir = TempDirectory.createTempDirectory();
        // System.err.println("Temp dir is "+tempDir.getAbsolutePath());
        pd.progressUpdated("Writing R script", 0, 1);
        // Get the template script
        Template template = new Template(ClassLoader.getSystemResource("uk/ac/babraham/SeqMonk/Displays/PCAPlot/pca_template.r"));
        // Substitute in the variables we need to change
        template.setValue("WORKING", tempDir.getAbsolutePath().replace("\\", "/"));
        // Write the script file
        File scriptFile = new File(tempDir.getAbsoluteFile() + "/script.r");
        PrintWriter pr = new PrintWriter(scriptFile);
        pr.print(template.toString());
        pr.close();
        // Write the count data
        File countFile = new File(tempDir.getAbsoluteFile() + "/data.txt");
        pr = new PrintWriter(countFile);
        StringBuffer sb;
        pd.progressUpdated("Writing count data", 0, 1);
        for (int p = 0; p < usedProbes.length; p++) {
            sb = new StringBuffer();
            for (int d = 0; d < stores.length; d++) {
                if (d > 0)
                    sb.append("\t");
                sb.append(stores[d].getValueForProbe(usedProbes[p]));
            }
            pr.println(sb.toString());
        }
        pr.close();
        pd.progressUpdated("Running R Script", 0, 1);
        RScriptRunner runner = new RScriptRunner(tempDir);
        RProgressListener listener = new RProgressListener(runner);
        runner.addProgressListener(new ProgressRecordDialog("R Session", runner));
        runner.runScript();
        while (true) {
            if (listener.cancelled()) {
                pd.progressCancelled();
                return;
            }
            if (listener.exceptionReceived()) {
                pd.progressExceptionReceived(listener.exception());
                return;
            }
            if (listener.complete())
                break;
            Thread.sleep(500);
        }
        // We can now parse and store the results
        File varianceFile = new File(tempDir.getAbsolutePath() + "/variance_data.txt");
        BufferedReader br = new BufferedReader(new FileReader(varianceFile));
        String line = br.readLine();
        FloatVector varianceVector = new FloatVector();
        while ((line = br.readLine()) != null) {
            String[] sections = line.split("\t");
            varianceVector.add(Float.parseFloat(sections[1]));
        }
        br.close();
        variances = varianceVector.toArray();
        // We want to convert the variance values into percentages
        // since that's what everyone wants for PCA.
        float varianceTotal = 0;
        for (int v = 0; v < variances.length; v++) {
            varianceTotal += variances[v];
        }
        // Now convert to percentages
        for (int v = 0; v < variances.length; v++) {
            variances[v] = (variances[v] / varianceTotal) * 100;
        }
        // We don't want to keep more than 10PCs
        if (variances.length > 10) {
            float[] shortVariances = new float[10];
            for (int i = 0; i < shortVariances.length; i++) {
                shortVariances[i] = variances[i];
            }
            variances = shortVariances;
        }
        File pcaFile = new File(tempDir.getAbsolutePath() + "/pca_data.txt");
        br = new BufferedReader(new FileReader(pcaFile));
        line = br.readLine();
        pcaResults = new float[stores.length][variances.length];
        int storeIndex = 0;
        while ((line = br.readLine()) != null) {
            String[] sections = line.split("\t");
            for (int i = 0; i < variances.length; i++) {
                pcaResults[storeIndex][i] = Float.parseFloat(sections[i + 1]);
            }
            ++storeIndex;
        }
        br.close();
        File weightsFile = new File(tempDir.getAbsolutePath() + "/pca_weights.txt");
        br = new BufferedReader(new FileReader(weightsFile));
        line = br.readLine();
        pcaRotations = new float[usedProbes.length][variances.length];
        int probeIndex = 0;
        while ((line = br.readLine()) != null) {
            String[] sections = line.split("\t");
            for (int i = 0; i < variances.length; i++) {
                // We multiply by 1000 to get the values on a vaguely
                // sane scale
                pcaRotations[probeIndex][i] = Float.parseFloat(sections[i + 1]) * 1000;
            }
            ++probeIndex;
        }
        br.close();
        runner.cleanUp();
    } catch (Exception e) {
        pd.progressExceptionReceived(e);
        return;
    }
    pd.progressComplete("pca_analysis", this);
    // TODO: Open the graph of variances and PCA results
    new PCAScatterPlotDialog(this);
    new PCAVarianceDialog(this);
}