Examples with PeakListRow net.sf.mzmine.datamodel.PeakListRow used on opensource projects

Example 16 with PeakListRow

use of net.sf.mzmine.datamodel.PeakListRow in project mzmine2 by mzmine.

the class PeakListRowLearnerTask method copyPeakRow.

/**
 * Create a copy of a feature list row.
 *
 * @param row the row to copy.
 * @return the newly created copy.
 */
private static PeakListRow copyPeakRow(final PeakListRow row) {
    // Copy the feature list row.
    final PeakListRow newRow = new SimplePeakListRow(row.getID());
    PeakUtils.copyPeakListRowProperties(row, newRow);
    // Copy the peaks.
    for (final Feature peak : row.getPeaks()) {
        final Feature newPeak = new SimpleFeature(peak);
        PeakUtils.copyPeakProperties(peak, newPeak);
        newRow.addPeak(peak.getDataFile(), newPeak);
    }
    return newRow;
}

Example 17 with PeakListRow

use of net.sf.mzmine.datamodel.PeakListRow in project mzmine2 by mzmine.

the class HeatMapTask method groupingDataset.

private double[][] groupingDataset(UserParameter<?, ?> selectedParameter, String referenceGroup) {
    // Collect all data files
    Vector<RawDataFile> allDataFiles = new Vector<RawDataFile>();
    DescriptiveStatistics meanControlStats = new DescriptiveStatistics();
    DescriptiveStatistics meanGroupStats = new DescriptiveStatistics();
    allDataFiles.addAll(Arrays.asList(peakList.getRawDataFiles()));
    // Determine the reference group and non reference group (the rest of
    // the samples) for raw data files
    List<RawDataFile> referenceDataFiles = new ArrayList<RawDataFile>();
    List<RawDataFile> nonReferenceDataFiles = new ArrayList<RawDataFile>();
    List<String> groups = new ArrayList<String>();
    for (RawDataFile rawDataFile : allDataFiles) {
        Object paramValue = project.getParameterValue(selectedParameter, rawDataFile);
        if (!groups.contains(String.valueOf(paramValue))) {
            groups.add(String.valueOf(paramValue));
        }
        if (String.valueOf(paramValue).equals(referenceGroup)) {
            referenceDataFiles.add(rawDataFile);
        } else {
            nonReferenceDataFiles.add(rawDataFile);
        }
    }
    int numRows = 0;
    for (int row = 0; row < peakList.getNumberOfRows(); row++) {
        if (!onlyIdentified || (onlyIdentified && peakList.getRow(row).getPeakIdentities().length > 0)) {
            numRows++;
        }
    }
    // Create a new aligned feature list with all the samples if the reference
    // group has to be shown or with only
    // the non reference group if not.
    double[][] dataMatrix = new double[groups.size() - 1][numRows];
    pValueMatrix = new String[groups.size() - 1][numRows];
    // data files that should be in the heat map
    List<RawDataFile> shownDataFiles = nonReferenceDataFiles;
    for (int row = 0, rowIndex = 0; row < peakList.getNumberOfRows(); row++) {
        PeakListRow rowPeak = peakList.getRow(row);
        if (!onlyIdentified || (onlyIdentified && rowPeak.getPeakIdentities().length > 0)) {
            // Average area or height of the reference group
            meanControlStats.clear();
            for (int column = 0; column < referenceDataFiles.size(); column++) {
                if (rowPeak.getPeak(referenceDataFiles.get(column)) != null) {
                    if (area) {
                        meanControlStats.addValue(rowPeak.getPeak(referenceDataFiles.get(column)).getArea());
                    } else {
                        meanControlStats.addValue(rowPeak.getPeak(referenceDataFiles.get(column)).getHeight());
                    }
                }
            }
            // Divide the area or height of each peak by the average of the
            // area or height of the reference peaks in each row
            int columnIndex = 0;
            for (int column = 0; column < groups.size(); column++) {
                String group = groups.get(column);
                meanGroupStats.clear();
                if (!group.equals(referenceGroup)) {
                    for (int dataColumn = 0; dataColumn < shownDataFiles.size(); dataColumn++) {
                        Object paramValue = project.getParameterValue(selectedParameter, shownDataFiles.get(dataColumn));
                        if (rowPeak.getPeak(shownDataFiles.get(dataColumn)) != null && String.valueOf(paramValue).equals(group)) {
                            Feature peak = rowPeak.getPeak(shownDataFiles.get(dataColumn));
                            if (!Double.isInfinite(peak.getArea()) && !Double.isNaN(peak.getArea())) {
                                if (area) {
                                    meanGroupStats.addValue(peak.getArea());
                                } else {
                                    meanGroupStats.addValue(peak.getHeight());
                                }
                            }
                        }
                    }
                    double value = meanGroupStats.getMean() / meanControlStats.getMean();
                    if (meanGroupStats.getN() > 1 && meanControlStats.getN() > 1) {
                        pValueMatrix[columnIndex][rowIndex] = this.getPvalue(meanGroupStats, meanControlStats);
                    } else {
                        pValueMatrix[columnIndex][rowIndex] = "";
                    }
                    if (log) {
                        value = Math.log(value);
                    }
                    dataMatrix[columnIndex++][rowIndex] = value;
                }
            }
            rowIndex++;
        }
    }
    // deviation of each column
    if (scale) {
        scale(dataMatrix);
    }
    // Create two arrays: row and column names
    rowNames = new String[dataMatrix[0].length];
    colNames = new String[groups.size() - 1];
    int columnIndex = 0;
    for (String group : groups) {
        if (!group.equals(referenceGroup)) {
            colNames[columnIndex++] = group;
        }
    }
    for (int row = 0, rowIndex = 0; row < peakList.getNumberOfRows(); row++) {
        if (!onlyIdentified || (onlyIdentified && peakList.getRow(row).getPeakIdentities().length > 0)) {
            if (peakList.getRow(row).getPeakIdentities() != null && peakList.getRow(row).getPeakIdentities().length > 0) {
                rowNames[rowIndex++] = peakList.getRow(row).getPreferredPeakIdentity().getName();
            } else {
                rowNames[rowIndex++] = "Unknown";
            }
        }
    }
    return dataMatrix;
}

Example 18 with PeakListRow

use of net.sf.mzmine.datamodel.PeakListRow in project mzmine2 by mzmine.

the class SammonsDataset method run.

@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);
    if (selectedRows.length == 0) {
        this.status = TaskStatus.ERROR;
        errorMessage = "No peaks selected for Sammons plot";
        return;
    }
    if (selectedRawDataFiles.length == 0) {
        this.status = TaskStatus.ERROR;
        errorMessage = "No raw data files selected for Sammons plot";
        return;
    }
    logger.info("Computing projection plot");
    // Generate matrix of raw data (input to Sammon's projection)
    boolean useArea = false;
    if (parameters.getParameter(ProjectionPlotParameters.peakMeasurementType).getValue() == PeakMeasurementType.AREA)
        useArea = true;
    double[][] rawData = new double[selectedRawDataFiles.length][selectedRows.length];
    for (int rowIndex = 0; rowIndex < selectedRows.length; rowIndex++) {
        PeakListRow peakListRow = selectedRows[rowIndex];
        for (int fileIndex = 0; fileIndex < selectedRawDataFiles.length; fileIndex++) {
            RawDataFile rawDataFile = selectedRawDataFiles[fileIndex];
            Feature p = peakListRow.getPeak(rawDataFile);
            if (p != null) {
                if (useArea)
                    rawData[fileIndex][rowIndex] = p.getArea();
                else
                    rawData[fileIndex][rowIndex] = p.getHeight();
            }
        }
    }
    int numComponents = xAxisDimension;
    if (yAxisDimension > numComponents)
        numComponents = yAxisDimension;
    // Scale data and do Sammon's mapping
    Preprocess.scaleToUnityVariance(rawData);
    Sammons sammonsProj = new Sammons(rawData);
    projectionStatus = sammonsProj.getProjectionStatus();
    sammonsProj.iterate(100);
    if (status == TaskStatus.CANCELED)
        return;
    double[][] result = sammonsProj.getState();
    if (status == TaskStatus.CANCELED)
        return;
    component1Coords = result[xAxisDimension - 1];
    component2Coords = result[yAxisDimension - 1];
    ProjectionPlotWindow newFrame = new ProjectionPlotWindow(peakList, this, parameters);
    newFrame.setVisible(true);
    setStatus(TaskStatus.FINISHED);
    logger.info("Finished computing projection plot.");
}

Example 19 with PeakListRow

use of net.sf.mzmine.datamodel.PeakListRow in project mzmine2 by mzmine.

the class PeakLearnerTask method run.

/**
 * @see Runnable#run()
 */
@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Running learner task on " + peakList);
    // Create a new results peakList which is added at the end
    resultPeakList = new SimplePeakList(peakList + " " + suffix, peakList.getRawDataFiles());
    /**
     * - A PeakList is a list of Features (peak in retention time dimension with accurate m/z)<br>
     * ---- contains one or multiple RawDataFiles <br>
     * ---- access mean retention time, mean m/z, maximum intensity, ...<br>
     * - A RawDataFile holds a full chromatographic run with all ms scans<br>
     * ---- Each Scan and the underlying raw data can be accessed <br>
     * ---- Scans can be filtered by MS level, polarity, ...<br>
     */
    // is the data provided by peaklist enough for this task or
    // do you want to work on one raw data file or on all files?
    RawDataFile dataFile = peakList.getRawDataFile(0);
    // get all peaks of a raw data file
    // Sort peaks by ascending mz
    Feature[] sortedPeaks = peakList.getPeaks(dataFile);
    Arrays.sort(sortedPeaks, new PeakSorter(SortingProperty.MZ, SortingDirection.Ascending));
    // Loop through all peaks
    totalPeaks = sortedPeaks.length;
    for (int i = 0; i < totalPeaks; i++) {
        // check for cancelled state and stop
        if (isCanceled())
            return;
        // current peak
        Feature aPeak = sortedPeaks[i];
        // do stuff
        // ...
        // add row to result feature list
        PeakListRow row = peakList.getPeakRow(aPeak);
        row = copyPeakRow(row);
        resultPeakList.addRow(row);
        // Update completion rate
        processedPeaks++;
    }
    // add to project
    addResultToProject();
    logger.info("Finished on " + peakList);
    setStatus(TaskStatus.FINISHED);
}

Example 20 with PeakListRow

use of net.sf.mzmine.datamodel.PeakListRow in project mzmine2 by mzmine.

the class StreamPeakListRowLearnerTask method copyPeakRow.

/**
 * Create a copy of a feature list row.
 *
 * @param row the row to copy.
 * @return the newly created copy.
 */
private static PeakListRow copyPeakRow(final PeakListRow row) {
    // Copy the feature list row.
    final PeakListRow newRow = new SimplePeakListRow(row.getID());
    PeakUtils.copyPeakListRowProperties(row, newRow);
    // Copy the peaks.
    for (final Feature peak : row.getPeaks()) {
        final Feature newPeak = new SimpleFeature(peak);
        PeakUtils.copyPeakProperties(peak, newPeak);
        newRow.addPeak(peak.getDataFile(), newPeak);
    }
    return newRow;
}