Examples with SimplePeakListAppliedMethod net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod used on opensource projects

Example 36 with SimplePeakListAppliedMethod

use of net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod in project mzmine2 by mzmine.

the class DeconvolutionTask method resolvePeaks.

/**
 * Deconvolve a chromatogram into separate peaks.
 *
 * @param peakList holds the chromatogram to deconvolve.
 * @param mzCenterFunction2
 * @return a new feature list holding the resolved peaks.
 * @throws RSessionWrapperException
 */
private PeakList resolvePeaks(final PeakList peakList, RSessionWrapper rSession) throws RSessionWrapperException {
    // Get data file information.
    final RawDataFile dataFile = peakList.getRawDataFile(0);
    // Peak resolver.
    final MZmineProcessingStep<PeakResolver> resolver = parameters.getParameter(PEAK_RESOLVER).getValue();
    // set msms pairing range
    this.setMSMSRange = parameters.getParameter(mzRangeMSMS).getValue();
    if (setMSMSRange)
        this.msmsRange = parameters.getParameter(mzRangeMSMS).getEmbeddedParameter().getValue();
    else
        this.msmsRange = 0;
    this.setMSMSRT = parameters.getParameter(RetentionTimeMSMS).getValue();
    if (setMSMSRT)
        this.RTRangeMSMS = parameters.getParameter(RetentionTimeMSMS).getEmbeddedParameter().getValue();
    else
        this.RTRangeMSMS = 0;
    // Create new feature list.
    final PeakList resolvedPeaks = new SimplePeakList(peakList + " " + parameters.getParameter(SUFFIX).getValue(), dataFile);
    // Load previous applied methods.
    for (final PeakListAppliedMethod method : peakList.getAppliedMethods()) {
        resolvedPeaks.addDescriptionOfAppliedTask(method);
    }
    // Add task description to feature list.
    resolvedPeaks.addDescriptionOfAppliedTask(new SimplePeakListAppliedMethod("Peak deconvolution by " + resolver, resolver.getParameterSet()));
    // Initialise counters.
    processedRows = 0;
    totalRows = peakList.getNumberOfRows();
    int peakId = 1;
    // Process each chromatogram.
    final PeakListRow[] peakListRows = peakList.getRows();
    final int chromatogramCount = peakListRows.length;
    for (int index = 0; !isCanceled() && index < chromatogramCount; index++) {
        final PeakListRow currentRow = peakListRows[index];
        final Feature chromatogram = currentRow.getPeak(dataFile);
        // Resolve peaks.
        final PeakResolver resolverModule = resolver.getModule();
        final ParameterSet resolverParams = resolver.getParameterSet();
        final ResolvedPeak[] peaks = resolverModule.resolvePeaks(chromatogram, resolverParams, rSession, mzCenterFunction, msmsRange, RTRangeMSMS);
        // Add peaks to the new feature list.
        for (final ResolvedPeak peak : peaks) {
            peak.setParentChromatogramRowID(currentRow.getID());
            final PeakListRow newRow = new SimplePeakListRow(peakId++);
            newRow.addPeak(dataFile, peak);
            newRow.setPeakInformation(peak.getPeakInformation());
            resolvedPeaks.addRow(newRow);
        }
        processedRows++;
    }
    return resolvedPeaks;
}

Example 37 with SimplePeakListAppliedMethod

use of net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod in project mzmine2 by mzmine.

the class CameraSearchTask method groupPeaksByPCGroup.

/**
 * Uses PCGroup-field in PeakIdentity to group peaks and build spectrum
 *
 * @param peakList a PeakList object
 * @return a new PeakList object
 */
private PeakList groupPeaksByPCGroup(PeakList peakList) {
    // Create new feature list.
    final PeakList combinedPeakList = new SimplePeakList(peakList + " " + parameters.getParameter(CameraSearchParameters.SUFFIX).getValue(), peakList.getRawDataFiles());
    // Load previous applied methods.
    for (final PeakList.PeakListAppliedMethod method : peakList.getAppliedMethods()) {
        combinedPeakList.addDescriptionOfAppliedTask(method);
    }
    // Add task description to feature list.
    combinedPeakList.addDescriptionOfAppliedTask(new SimplePeakListAppliedMethod("Bioconductor CAMERA", parameters));
    // --------------------
    // Find unique PCGroups
    // --------------------
    Set<String> pcGroups = new HashSet<>();
    for (PeakListRow row : peakList.getRows()) {
        PeakIdentity identity = row.getPreferredPeakIdentity();
        if (identity == null)
            continue;
        String groupName = identity.getName();
        if (groupName == null || groupName.length() == 0)
            continue;
        pcGroups.add(groupName);
    }
    List<PeakListRow> groupRows = new ArrayList<>();
    // Set <String> groupNames = new HashSet <> ();
    Map<Double, Double> spectrum = new HashMap<>();
    List<PeakListRow> newPeakListRows = new ArrayList<>();
    for (String groupName : pcGroups) {
        // -----------------------------------------
        // Find all peaks belonging to isotopeGroups
        // -----------------------------------------
        groupRows.clear();
        // groupNames.clear();
        spectrum.clear();
        double maxIntensity = 0.0;
        PeakListRow groupRow = null;
        for (PeakListRow row : peakList.getRows()) {
            PeakIdentity identity = row.getPreferredPeakIdentity();
            if (identity == null)
                continue;
            String name = identity.getName();
            if (name.equals(groupName)) {
                double intensity = row.getAverageHeight();
                groupRows.add(row);
                // groupNames.add(name);
                spectrum.put(row.getAverageMZ(), intensity);
                if (intensity > maxIntensity) {
                    maxIntensity = intensity;
                    groupRow = row;
                }
            }
        }
        if (groupRow == null || spectrum.size() <= 1)
            continue;
        PeakIdentity identity = groupRow.getPreferredPeakIdentity();
        if (identity == null)
            continue;
        DataPoint[] dataPoints = new DataPoint[spectrum.size()];
        int count = 0;
        for (Entry<Double, Double> e : spectrum.entrySet()) dataPoints[count++] = new SimpleDataPoint(e.getKey(), e.getValue());
        IsotopePattern pattern = new SimpleIsotopePattern(dataPoints, IsotopePatternStatus.PREDICTED, "Spectrum");
        groupRow.getBestPeak().setIsotopePattern(pattern);
        // combinedPeakList.addRow(groupRow);
        newPeakListRows.add(groupRow);
    }
    // ------------------------------------
    // Sort new peak rows by retention time
    // ------------------------------------
    Collections.sort(newPeakListRows, new Comparator<PeakListRow>() {

        @Override
        public int compare(PeakListRow row1, PeakListRow row2) {
            double retTime1 = row1.getAverageRT();
            double retTime2 = row2.getAverageRT();
            return Double.compare(retTime1, retTime2);
        }
    });
    for (PeakListRow row : newPeakListRows) combinedPeakList.addRow(row);
    return combinedPeakList;
}

Example 38 with SimplePeakListAppliedMethod

use of net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod in project mzmine2 by mzmine.

the class ComplexSearchTask method run.

/**
 * @see java.lang.Runnable#run()
 */
public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Starting complex search in " + peakList);
    PeakListRow[] rows = peakList.getRows();
    totalRows = rows.length;
    // Sort the array by m/z so we start with biggest peak (possible
    // complex)
    Arrays.sort(rows, new PeakListRowSorter(SortingProperty.MZ, SortingDirection.Descending));
    // Compare each three rows against each other
    for (int i = 0; i < totalRows; i++) {
        Range<Double> testRTRange = rtTolerance.getToleranceRange(rows[i].getAverageRT());
        PeakListRow[] testRows = peakList.getRowsInsideScanRange(testRTRange);
        for (int j = 0; j < testRows.length; j++) {
            for (int k = j; k < testRows.length; k++) {
                // Task canceled?
                if (isCanceled())
                    return;
                // very small m/z peak
                if ((rows[i] == testRows[j]) || (rows[i] == testRows[k]))
                    continue;
                if (checkComplex(rows[i], testRows[j], testRows[k]))
                    addComplexInfo(rows[i], testRows[j], testRows[k]);
            }
        }
        finishedRows++;
    }
    // Add task description to peakList
    ((SimplePeakList) peakList).addDescriptionOfAppliedTask(new SimplePeakListAppliedMethod("Identification of complexes", parameters));
    // Repaint the window to reflect the change in the feature list
    Desktop desktop = MZmineCore.getDesktop();
    if (!(desktop instanceof HeadLessDesktop))
        desktop.getMainWindow().repaint();
    setStatus(TaskStatus.FINISHED);
    logger.info("Finished complexes search in " + peakList);
}

Example 39 with SimplePeakListAppliedMethod

use of net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod in project mzmine2 by mzmine.

the class LinearNormalizerTask method run.

public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Running linear normalizer");
    // This hashtable maps rows from original alignment result to rows of
    // the normalized alignment
    Hashtable<PeakListRow, SimplePeakListRow> rowMap = new Hashtable<PeakListRow, SimplePeakListRow>();
    // Create new feature list
    normalizedPeakList = new SimplePeakList(originalPeakList + " " + suffix, originalPeakList.getRawDataFiles());
    // Loop through all raw data files, and find the peak with biggest
    // height
    double maxOriginalHeight = 0.0;
    for (RawDataFile file : originalPeakList.getRawDataFiles()) {
        for (PeakListRow originalpeakListRow : originalPeakList.getRows()) {
            Feature p = originalpeakListRow.getPeak(file);
            if (p != null) {
                if (maxOriginalHeight <= p.getHeight())
                    maxOriginalHeight = p.getHeight();
            }
        }
    }
    // Loop through all raw data files, and normalize peak values
    for (RawDataFile file : originalPeakList.getRawDataFiles()) {
        // Cancel?
        if (isCanceled()) {
            return;
        }
        // Determine normalization type and calculate normalization factor
        double normalizationFactor = 1.0;
        // - normalization by average peak intensity
        if (normalizationType == NormalizationType.AverageIntensity) {
            double intensitySum = 0;
            int intensityCount = 0;
            for (PeakListRow peakListRow : originalPeakList.getRows()) {
                Feature p = peakListRow.getPeak(file);
                if (p != null) {
                    if (peakMeasurementType == PeakMeasurementType.HEIGHT) {
                        intensitySum += p.getHeight();
                    } else {
                        intensitySum += p.getArea();
                    }
                    intensityCount++;
                }
            }
            normalizationFactor = intensitySum / (double) intensityCount;
        }
        // - normalization by average squared peak intensity
        if (normalizationType == NormalizationType.AverageSquaredIntensity) {
            double intensitySum = 0.0;
            int intensityCount = 0;
            for (PeakListRow peakListRow : originalPeakList.getRows()) {
                Feature p = peakListRow.getPeak(file);
                if (p != null) {
                    if (peakMeasurementType == PeakMeasurementType.HEIGHT) {
                        intensitySum += (p.getHeight() * p.getHeight());
                    } else {
                        intensitySum += (p.getArea() * p.getArea());
                    }
                    intensityCount++;
                }
            }
            normalizationFactor = intensitySum / (double) intensityCount;
        }
        // - normalization by maximum peak intensity
        if (normalizationType == NormalizationType.MaximumPeakHeight) {
            double maximumIntensity = 0.0;
            for (PeakListRow peakListRow : originalPeakList.getRows()) {
                Feature p = peakListRow.getPeak(file);
                if (p != null) {
                    if (peakMeasurementType == PeakMeasurementType.HEIGHT) {
                        if (maximumIntensity < p.getHeight())
                            maximumIntensity = p.getHeight();
                    } else {
                        if (maximumIntensity < p.getArea())
                            maximumIntensity = p.getArea();
                    }
                }
            }
            normalizationFactor = maximumIntensity;
        }
        // - normalization by total raw signal
        if (normalizationType == NormalizationType.TotalRawSignal) {
            normalizationFactor = 0;
            for (int scanNumber : file.getScanNumbers(1)) {
                Scan scan = file.getScan(scanNumber);
                normalizationFactor += scan.getTIC();
            }
        }
        // Readjust normalization factor so that maximum height will be
        // equal to maximumOverallPeakHeightAfterNormalization after
        // normalization
        double maxNormalizedHeight = maxOriginalHeight / normalizationFactor;
        normalizationFactor = normalizationFactor * maxNormalizedHeight / maximumOverallPeakHeightAfterNormalization;
        // Normalize all peak intenisities using the normalization factor
        for (PeakListRow originalpeakListRow : originalPeakList.getRows()) {
            // Cancel?
            if (isCanceled()) {
                return;
            }
            Feature originalPeak = originalpeakListRow.getPeak(file);
            if (originalPeak != null) {
                SimpleFeature normalizedPeak = new SimpleFeature(originalPeak);
                PeakUtils.copyPeakProperties(originalPeak, normalizedPeak);
                double normalizedHeight = originalPeak.getHeight() / normalizationFactor;
                double normalizedArea = originalPeak.getArea() / normalizationFactor;
                normalizedPeak.setHeight(normalizedHeight);
                normalizedPeak.setArea(normalizedArea);
                SimplePeakListRow normalizedRow = rowMap.get(originalpeakListRow);
                if (normalizedRow == null) {
                    normalizedRow = new SimplePeakListRow(originalpeakListRow.getID());
                    PeakUtils.copyPeakListRowProperties(originalpeakListRow, normalizedRow);
                    rowMap.put(originalpeakListRow, normalizedRow);
                }
                normalizedRow.addPeak(file, normalizedPeak);
            }
        }
        // Progress
        processedDataFiles++;
    }
    // Finally add all normalized rows to normalized alignment result
    for (PeakListRow originalpeakListRow : originalPeakList.getRows()) {
        SimplePeakListRow normalizedRow = rowMap.get(originalpeakListRow);
        if (normalizedRow == null)
            continue;
        normalizedPeakList.addRow(normalizedRow);
    }
    // Add new peaklist to the project
    project.addPeakList(normalizedPeakList);
    // Load previous applied methods
    for (PeakListAppliedMethod proc : originalPeakList.getAppliedMethods()) {
        normalizedPeakList.addDescriptionOfAppliedTask(proc);
    }
    // Add task description to peakList
    normalizedPeakList.addDescriptionOfAppliedTask(new SimplePeakListAppliedMethod("Linear normalization of by " + normalizationType, parameters));
    // Remove the original peaklist if requested
    if (removeOriginal)
        project.removePeakList(originalPeakList);
    logger.info("Finished linear normalizer");
    setStatus(TaskStatus.FINISHED);
}

Example 40 with SimplePeakListAppliedMethod

use of net.sf.mzmine.datamodel.impl.SimplePeakListAppliedMethod in project mzmine2 by mzmine.

the class RTCalibrationTask method run.

public void run() {
    setStatus(TaskStatus.PROCESSING);
    logger.info("Running retention time normalizer");
    // First we need to find standards by iterating through first feature list
    totalRows = originalPeakLists[0].getNumberOfRows();
    // Create new feature lists
    normalizedPeakLists = new SimplePeakList[originalPeakLists.length];
    for (int i = 0; i < originalPeakLists.length; i++) {
        normalizedPeakLists[i] = new SimplePeakList(originalPeakLists[i] + " " + suffix, originalPeakLists[i].getRawDataFiles());
        // Remember how many rows we need to normalize
        totalRows += originalPeakLists[i].getNumberOfRows();
    }
    // goodStandards Vector contains identified standard rows, represented
    // by arrays. Each array has same length as originalPeakLists array.
    // Array items represent particular standard peak in each PeakList
    Vector<PeakListRow[]> goodStandards = new Vector<PeakListRow[]>();
    // Iterate the first peaklist
    standardIteration: for (PeakListRow candidate : originalPeakLists[0].getRows()) {
        // Cancel?
        if (isCanceled()) {
            return;
        }
        processedRows++;
        // Check that all peaks of this row have proper height
        for (Feature p : candidate.getPeaks()) {
            if (p.getHeight() < minHeight)
                continue standardIteration;
        }
        PeakListRow[] goodStandardCandidate = new PeakListRow[originalPeakLists.length];
        goodStandardCandidate[0] = candidate;
        double candidateMZ = candidate.getAverageMZ();
        double candidateRT = candidate.getAverageRT();
        // Find matching rows in remaining peaklists
        for (int i = 1; i < originalPeakLists.length; i++) {
            Range<Double> rtRange = rtTolerance.getToleranceRange(candidateRT);
            Range<Double> mzRange = mzTolerance.getToleranceRange(candidateMZ);
            PeakListRow[] matchingRows = originalPeakLists[i].getRowsInsideScanAndMZRange(rtRange, mzRange);
            // standard candidate
            if (matchingRows.length != 1)
                continue standardIteration;
            // Check that all peaks of this row have proper height
            for (Feature p : matchingRows[0].getPeaks()) {
                if (p.getHeight() < minHeight)
                    continue standardIteration;
            }
            // Save reference to matching peak in this feature list
            goodStandardCandidate[i] = matchingRows[0];
        }
        // If we found a match of same peak in all peaklists, mark it as a
        // good standard
        goodStandards.add(goodStandardCandidate);
        logger.finest("Found a good standard for RT normalization: " + candidate);
    }
    // Check if we have any standards
    if (goodStandards.size() == 0) {
        setStatus(TaskStatus.ERROR);
        setErrorMessage("No good standard peak was found");
        return;
    }
    // Calculate average retention times of all standards
    double[] averagedRTs = new double[goodStandards.size()];
    for (int i = 0; i < goodStandards.size(); i++) {
        double rtAverage = 0;
        for (PeakListRow row : goodStandards.get(i)) rtAverage += row.getAverageRT();
        rtAverage /= (double) originalPeakLists.length;
        averagedRTs[i] = rtAverage;
    }
    // Normalize each feature list
    for (int peakListIndex = 0; peakListIndex < originalPeakLists.length; peakListIndex++) {
        // Get standard rows for this feature list only
        PeakListRow[] standards = new PeakListRow[goodStandards.size()];
        for (int i = 0; i < goodStandards.size(); i++) {
            standards[i] = goodStandards.get(i)[peakListIndex];
        }
        normalizePeakList(originalPeakLists[peakListIndex], normalizedPeakLists[peakListIndex], standards, averagedRTs);
    }
    // Cancel?
    if (isCanceled()) {
        return;
    }
    for (int i = 0; i < originalPeakLists.length; i++) {
        project.addPeakList(normalizedPeakLists[i]);
        // Load previous applied methods
        for (PeakListAppliedMethod proc : originalPeakLists[i].getAppliedMethods()) {
            normalizedPeakLists[i].addDescriptionOfAppliedTask(proc);
        }
        // Add task description to peakList
        normalizedPeakLists[i].addDescriptionOfAppliedTask(new SimplePeakListAppliedMethod("Retention time normalization", parameters));
        // Remove the original peaklists if requested
        if (removeOriginal)
            project.removePeakList(originalPeakLists[i]);
    }
    logger.info("Finished retention time normalizer");
    setStatus(TaskStatus.FINISHED);
}