private void setClusters(int[][] sampleArray) {
if (sampleArray == null || sampleArray.length == 0) sampleArray = new int[1][];
clusterCount = sampleArray.length;
clusterNames = new String[clusterCount];
clusterColors = new Color[clusterCount];
clusters = new int[clusterCount][];
for (int i = 0; i < clusterCount; i++) {
if (sampleArray[i] == null) {
int numSamps = experiment.getNumberOfSamples();
clusters[i] = new int[numSamps];
for (int j = 0; j < numSamps; j++) {
clusters[i][j] = j;
}
clusterColors[i] = Color.white;
clusterNames[i] = "All Samples";
} else {
clusters[i] = sampleArray[i];
clusterColors[i] = createClusterColor(i);
if (sampleClusterNames == null || sampleClusterNames[i].length() < 1) {
clusterNames[i] = "(Group " + (i + 1) + ")";
} else {
clusterNames[i] = sampleClusterNames[i];
}
}
}
}
public AlgorithmData getScriptParameters(IFramework framework) {
this.experiment = framework.getData().getExperiment();
exptNamesVector = new Vector<String>();
int number_of_samples = experiment.getNumberOfSamples();
// int number_of_genes = experiment.getNumberOfGenes();
for (int i = 0; i < number_of_samples; i++) {
exptNamesVector.add(framework.getData().getFullSampleName(experiment.getSampleIndex(i)));
}
DEGseqInitBox DEGseqDialog =
new DEGseqInitBox(
(JFrame) framework.getFrame(), true, exptNamesVector, framework.getClusterRepository(1)
// framework.getClusterRepository(0)
);
DEGseqDialog.setVisible(true);
if (!DEGseqDialog.isOkPressed()) return null;
if (DEGseqDialog.getTestDesign() == DEGseqInitBox.TWO_CLASS) {
if (DEGseqDialog.getSelectionDesign() == DEGseqInitBox.CLUSTER_SELECTION) {
groupAssignments = DEGseqDialog.getClusterTwoClassAssignments();
}
if (DEGseqDialog.getSelectionDesign() == DEGseqInitBox.BUTTON_SELECTION) {
groupAssignments = DEGseqDialog.getTwoClassAssignments();
}
}
AlgorithmData data = new AlgorithmData();
// alg name
data.addParam("name", "DESeq");
// alg type
data.addParam("alg-type", "data-visualization");
// output class
data.addParam("output-class", "partition-output");
// output nodes
String[] outputNodes = new String[2];
outputNodes[0] = "Significant Genes";
outputNodes[1] = "Non-significant Genes";
data.addStringArray("output-nodes", outputNodes);
return data;
}
public DefaultMutableTreeNode executeScript(
IFramework framework, AlgorithmData algData, Experiment experiment)
throws AlgorithmException {
Listener listener = new Listener();
this.experiment = experiment;
this.data = framework.getData();
// this.timeAssignments = algData.getIntArray("time_assignments");
this.groupAssignments = algData.getIntArray("condition_assignments");
exptNamesVector = new Vector<String>();
int number_of_samples = experiment.getNumberOfSamples();
for (int i = 0; i < number_of_samples; i++) {
exptNamesVector.add(this.data.getFullSampleName(i));
}
try {
algData.addMatrix("experiment", experiment.getMatrix());
algorithm = framework.getAlgorithmFactory().getAlgorithm("MINET");
algorithm.addAlgorithmListener(listener);
this.progress = new Progress(framework.getFrame(), "Running MINET Analysis...", listener);
this.progress.show();
long start = System.currentTimeMillis();
AlgorithmData result = algorithm.execute(algData);
long time = System.currentTimeMillis() - start;
AlgorithmParameters params = algData.getParams();
GeneralInfo info = new GeneralInfo();
return createResultTree(info);
} finally {
if (algorithm != null) {
algorithm.removeAlgorithmListener(listener);
}
if (progress != null) {
progress.dispose();
}
}
}
public void setClusters(Cluster[] clusterArray) {
if (clusterArray == null || clusterArray.length == 0) clusterArray = new Cluster[1];
clusterCount = clusterArray.length;
clusterNames = new String[clusterCount];
clusterColors = new Color[clusterCount];
clusters = new int[clusterCount][];
for (int i = 0; i < clusterCount; i++) {
if (clusterArray[i] == null) {
int numSamps = experiment.getNumberOfSamples();
clusters[i] = new int[numSamps];
for (int j = 0; j < numSamps; j++) {
clusters[i][j] = j;
}
clusterColors[i] = Color.white;
clusterNames[i] = "All Samples";
} else {
clusters[i] = clusterArray[i].getIndices();
clusterColors[i] = clusterArray[i].getClusterColor();
clusterNames[i] = clusterArray[i].getClusterLabel();
if (clusterNames[i].length() < 1) clusterNames[i] = "(Cluster " + (i + 1) + ")";
}
}
}
private void saveCluster(File file, Experiment experiment, IData data, int[] rows)
throws Exception {
PrintWriter out = new PrintWriter(new FileOutputStream(file));
String[] fieldNames = data.getFieldNames();
out.print("Original row");
out.print("\t");
for (int i = 0; i < fieldNames.length; i++) {
out.print(fieldNames[i]);
// if (i < fieldNames.length - 1) {
out.print("\t");
// }
}
for (int i = 0; i < geneGroupMeans[0].length; i++) {
out.print("Group" + (i + 1) + " mean\t");
out.print("Group" + (i + 1) + " std.dev.\t");
}
// out.print("\t");
out.print("F ratio");
out.print("\t");
out.print("SS(Groups)\t");
out.print("SS(Error)\t");
out.print("df (Groups)\t");
out.print("df (Error)\t");
out.print("raw p value\t");
out.print("adj p value");
// out.print("UniqueID\tName");
for (int i = 0; i < experiment.getNumberOfSamples(); i++) {
out.print("\t");
out.print(data.getFullSampleName(experiment.getSampleIndex(i)));
}
out.print("\n");
for (int i = 0; i < rows.length; i++) {
out.print(Integer.toString(experiment.getGeneIndexMappedToData(rows[i]) + 1));
// out.print(data.getUniqueId(rows[i]));
out.print("\t");
// out.print(data.getGeneName(rows[i]));
for (int k = 0; k < fieldNames.length; k++) {
out.print(data.getElementAttribute(experiment.getGeneIndexMappedToData(rows[i]), k));
// if (k < fieldNames.length - 1) {
out.print("\t");
// }
}
for (int j = 0; j < geneGroupMeans[rows[i]].length; j++) {
out.print(geneGroupMeans[rows[i]][j] + "\t");
out.print(geneGroupSDs[rows[i]][j] + "\t");
}
// out.print("\t");
out.print("" + ((Float) fValues.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) ssGroups.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) ssError.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) dfNumValues.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) dfDenomValues.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) rawPValues.get(rows[i])).floatValue());
out.print("\t");
out.print("" + ((Float) adjPValues.get(rows[i])).floatValue());
for (int j = 0; j < experiment.getNumberOfSamples(); j++) {
out.print("\t");
out.print(Float.toString(experiment.get(rows[i], j)));
}
out.print("\n");
}
out.flush();
out.close();
}