use of net.sf.mzmine.modules.peaklistmethods.dataanalysis.projectionplots.ProjectionPlotWindow in project mzmine2 by mzmine.
the class ClusteringTask method run.
@Override
public void run() {
status = TaskStatus.PROCESSING;
logger.info("Clustering");
double[][] rawData;
if (typeOfData == ClusteringDataType.VARIABLES) {
rawData = createMatrix(false);
dataset = createVariableWekaDataset(rawData);
} else {
rawData = createMatrix(true);
dataset = createSampleWekaDataset(rawData);
}
// Run the clustering algorithm
ClusteringAlgorithm clusteringAlgorithm = clusteringStep.getModule();
ParameterSet clusteringParameters = clusteringStep.getParameterSet();
ClusteringResult result = clusteringAlgorithm.performClustering(dataset, clusteringParameters);
String cluster = "";
if (clusteringAlgorithm.getName().toString().equals("Hierarchical clusterer")) {
progress = 0;
// Getting the result of the clustering in Newick format
cluster = result.getHiearchicalCluster();
// Getting the number of clusters counting the number of times the
// word "cluster" is in the result
Pattern p = Pattern.compile("Cluster", Pattern.LITERAL | Pattern.CASE_INSENSITIVE);
int numberOfClusters = p.split(cluster, -1).length - 1;
if (numberOfClusters == 0) {
numberOfClusters = 1;
}
// Visualization window for each cluster
for (int i = 0; i < numberOfClusters; i++) {
String c = null;
String clusterNumber = "Cluster " + i;
if (cluster.indexOf(clusterNumber) > 0) {
int nextNumber = i + 1;
String clusterNumber2 = "Cluster " + nextNumber;
if (cluster.indexOf(clusterNumber2) < 0) {
c = cluster.substring(cluster.indexOf(clusterNumber) + clusterNumber.length(), cluster.length());
} else {
c = cluster.substring(cluster.indexOf(clusterNumber) + clusterNumber.length(), cluster.indexOf(clusterNumber2));
}
} else {
c = cluster;
}
JFrame visualizationWindow = new JFrame(clusterNumber);
visualizationWindow.setSize(600, 500);
visualizationWindow.setLayout(new BorderLayout());
HierarchyVisualizer visualizer = new HierarchyVisualizer(c);
visualizationWindow.add(visualizer, BorderLayout.CENTER);
visualizer.fitToScreen();
// Text field with the clustering result in Newick format
JTextField data = new JTextField(c);
visualizationWindow.add(data, BorderLayout.SOUTH);
visualizationWindow.setVisible(true);
visualizationWindow.pack();
visualizationWindow.setVisible(true);
}
progress = 100;
} else {
List<Integer> clusteringResult = result.getClusters();
// Report window
Desktop desktop = MZmineCore.getDesktop();
if (typeOfData == ClusteringDataType.SAMPLES) {
String[] sampleNames = new String[selectedRawDataFiles.length];
for (int i = 0; i < selectedRawDataFiles.length; i++) {
sampleNames[i] = selectedRawDataFiles[i].getName();
}
ClusteringReportWindow reportWindow = new ClusteringReportWindow(sampleNames, clusteringResult.toArray(new Integer[0]), "Clustering Report");
reportWindow.setVisible(true);
} else {
String[] variableNames = new String[selectedRows.length];
for (int i = 0; i < selectedRows.length; i++) {
variableNames[i] = selectedRows[i].getID() + " - " + selectedRows[i].getAverageMZ() + " - " + selectedRows[i].getAverageRT();
if (selectedRows[i].getPeakIdentities() != null && selectedRows[i].getPeakIdentities().length > 0) {
variableNames[i] += " - " + selectedRows[i].getPeakIdentities()[0].getName();
}
}
ClusteringReportWindow reportWindow = new ClusteringReportWindow(variableNames, clusteringResult.toArray(new Integer[0]), "Clustering Report");
reportWindow.setVisible(true);
}
// Visualization
if (typeOfData == ClusteringDataType.VARIABLES) {
for (int ind = 0; ind < selectedRows.length; ind++) {
groupsForSelectedVariables[ind] = clusteringResult.get(ind);
}
} else {
for (int ind = 0; ind < selectedRawDataFiles.length; ind++) {
groupsForSelectedRawDataFiles[ind] = clusteringResult.get(ind);
}
}
this.finalNumberOfGroups = result.getNumberOfGroups();
parameterValuesForGroups = new Object[finalNumberOfGroups];
for (int i = 0; i < finalNumberOfGroups; i++) {
parameterValuesForGroups[i] = "Group " + i;
}
int numComponents = xAxisDimension;
if (yAxisDimension > numComponents) {
numComponents = yAxisDimension;
}
if (result.getVisualizationType() == VisualizationType.PCA) {
// Scale data and do PCA
Preprocess.scaleToUnityVariance(rawData);
PCA pcaProj = new PCA(rawData, numComponents);
projectionStatus = pcaProj.getProjectionStatus();
double[][] pcaResult = pcaProj.getState();
if (status == TaskStatus.CANCELED) {
return;
}
component1Coords = pcaResult[xAxisDimension - 1];
component2Coords = pcaResult[yAxisDimension - 1];
} else if (result.getVisualizationType() == VisualizationType.SAMMONS) {
// Scale data and do Sammon's mapping
Preprocess.scaleToUnityVariance(rawData);
Sammons sammonsProj = new Sammons(rawData);
projectionStatus = sammonsProj.getProjectionStatus();
sammonsProj.iterate(100);
double[][] sammonsResult = sammonsProj.getState();
if (status == TaskStatus.CANCELED) {
return;
}
component1Coords = sammonsResult[xAxisDimension - 1];
component2Coords = sammonsResult[yAxisDimension - 1];
}
ProjectionPlotWindow newFrame = new ProjectionPlotWindow(desktop.getSelectedPeakLists()[0], this, parameters);
newFrame.setVisible(true);
}
status = TaskStatus.FINISHED;
logger.info("Finished computing Clustering visualization.");
}
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