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();
}
}
}
/**
* This method should return a tree with calculation results or null, if analysis start was
* canceled.
*
* @param framework the reference to <code>IFramework</code> implementation, which is used to
* obtain an initial analysis data and parameters.
* @throws AlgorithmException if calculation was failed.
* @throws AbortException if calculation was canceled.
* @see IFramework
*/
public DefaultMutableTreeNode execute(IFramework framework) throws AlgorithmException {
if (sysMsg("R 2.11.x", "DEGSeq") != JOptionPane.OK_OPTION) return null;
this.data = framework.getData();
exptNamesVector = new Vector<String>();
for (int i = 0; i < this.data.getFeaturesCount(); i++) {
exptNamesVector.add(framework.getData().getFullSampleName(i));
}
DEGseqInitBox DEGseqDialog =
new DEGseqInitBox(
(JFrame) framework.getFrame(),
true,
exptNamesVector,
framework.getClusterRepository(1));
DEGseqDialog.setVisible(true);
if (!DEGseqDialog.isOkPressed()) return null;
infMethod = DEGseqDialog.getMethodName();
sigMethod = DEGseqDialog.getCutOffField();
sigCutOff = DEGseqDialog.getPValue();
dataDesign = DEGseqDialog.getTestDesign();
if (DEGseqDialog.getTestDesign() == DEGseqInitBox.TWO_CLASS) {
groupAssignments = DEGseqDialog.getTwoClassAssignments();
if (DEGseqDialog.getSelectionDesign() == DEGseqInitBox.CLUSTER_SELECTION) {
groupAssignments = DEGseqDialog.getClusterTwoClassAssignments();
}
if (DEGseqDialog.getSelectionDesign() == DEGseqInitBox.BUTTON_SELECTION) {
groupAssignments = DEGseqDialog.getTwoClassAssignments();
}
}
// count # of samples used in analysis
int samplesUsed = 0;
for (int i = 0; i < groupAssignments.length; i++) {
if (groupAssignments[i] != 0) samplesUsed++;
}
// get samples indices used
int sampleIndices[] = new int[samplesUsed];
for (int i = 0, ii = 0; i < groupAssignments.length; i++) {
if (groupAssignments[i] != 0) sampleIndices[ii++] = i;
}
// set up the group struct for algo
int[] twoClassGrps = new int[samplesUsed];
for (int i = 0, ii = 0; i < groupAssignments.length; i++) {
if (groupAssignments[i] != 0) twoClassGrps[ii++] = groupAssignments[i];
}
// Test
int grp1 = 0;
int grp2 = 0;
String grp1ColIndStr = "";
String grp2ColIndStr = "";
for (int i = 0; i < twoClassGrps.length; i++) {
if (twoClassGrps[i] == 1) {
grp1++;
grp1ColIndStr += String.valueOf(i + 2) + ","; // 1 for 0 based index, 1 for UID col in file
}
if (twoClassGrps[i] == 2) {
grp2++;
grp2ColIndStr += String.valueOf(i + 2) + ",";
}
}
// remove last ","
grp1ColIndStr = grp1ColIndStr.substring(0, grp1ColIndStr.lastIndexOf(","));
grp2ColIndStr = grp2ColIndStr.substring(0, grp2ColIndStr.lastIndexOf(","));
System.out.println("groupAssignments: " + Arrays.toString(groupAssignments));
System.out.println("twoClassGrps: " + Arrays.toString(twoClassGrps));
System.out.println(
samplesUsed
+ " out of "
+ groupAssignments.length
+ " used. Sample indices: "
+ Arrays.toString(sampleIndices));
System.out.println("grp1ColInd: " + grp1ColIndStr);
System.out.println("grp2ColInd: " + grp2ColIndStr);
this.experiment = framework.getData().getExperiment();
// int number_of_samples = this.data.getFeaturesCount();//experiment.getNumberOfSamples();
// int [] columnIndices = experiment.getColumnIndicesCopy();
sampleLabels = new ArrayList<String>();
geneLabels = new ArrayList<String>();
for (int i = 0; i < samplesUsed; i++) {
sampleLabels.add(framework.getData().getFullSampleName(sampleIndices[i])); // Raktim
}
// Raktim Use probe index as the gene labels in R
for (int i = 0; i < this.data.getFeaturesSize(); /*experiment.getNumberOfGenes();*/ i++) {
// geneLabels.add(framework.getData().getElementAnnotation(i,
// AnnotationFieldConstants.PROBE_ID)[0]); //Raktim
geneLabels.add(String.valueOf(i));
}
// Make Count Matrix based on data
int numGenes = this.data.getFeaturesSize();
System.out.println(
"data.getFeaturesCount(): "
+ this.data.getFeaturesCount()
+ " data.getFeaturesSize(): "
+ this.data.getFeaturesSize());
ArrayList<IRNASeqSlide> temp = (ArrayList<IRNASeqSlide>) data.getFeaturesList();
// TEst Code from EH
RNASeqChipAnnotation chipAnnotation = (RNASeqChipAnnotation) data.getChipAnnotation();
System.out.println("Library size, first sample: " + temp.get(0).getLibrarySize());
System.out.println("Library size, last sample: " + temp.get(temp.size() - 1).getLibrarySize());
System.out.println("\n");
System.out.println("Read length: " + chipAnnotation.getReadLength());
System.out.println("\n");
System.out.println("first count value for first slide: " + temp.get(0).getCount(0));
System.out.println(
"last count value for first slide: " + temp.get(0).getCount(temp.get(0).getSize() - 1));
System.out.println("\n");
System.out.println(
"first count value for last slide: " + temp.get(temp.size() - 1).getCount(0));
System.out.println(
"last count value for last slide: "
+ temp.get(temp.size() - 1).getCount(temp.get(0).getSize() - 1));
System.out.println("\n");
System.out.println(
"transcript length 0,0: "
+ ((RNASeqElement) (temp.get(0).getSlideDataElement(0))).getTranscriptLength());
System.out.println(
"classcode for 0,0: "
+ ((RNASeqElement) (temp.get(0).getSlideDataElement(0))).getClasscode());
// End EH Test Code
// My test code
// System.out.println("transcript length 0,0: " +
// ((RNASeqElement)(temp.get(0).getSlideDataElement(9))).getTranscriptLength());
// System.out.println("classcode for 0,0: " +
// ((RNASeqElement)(temp.get(0).getSlideDataElement(9))).getClasscode());
// End my test code
/** ArrayList<IRNASeqSlide> temp = (ArrayList<IRNASeqSlide>)data.getFeaturesList(); */
int[][] countMatrix = new int[numGenes][samplesUsed];
int[] libSize = new int[samplesUsed];
for (int row = 0; row < numGenes; row++) {
for (int col = 0; col < sampleIndices.length; col++) {
// get count value
countMatrix[row][col] = temp.get(sampleIndices[col]).getCount(row);
}
// get transcript len for each gene
// transcriptLen[row] =
// ((RNASeqElement)(temp.get(row).getSlideDataElement(row))).getTranscriptLength();
// transcriptLen[row] =
// ((RNASeqElement)(temp.get(0).getSlideDataElement(row))).getTranscriptLength();
}
// get lib size for each sample used
for (int col = 0; col < sampleIndices.length; col++) {
// get count value
libSize[col] = temp.get(sampleIndices[col]).getLibrarySize();
}
// System.out.println("CountMatrix: " + Arrays.deepToString(countMatrix));
// System.out.println("transcriptLen: " + Arrays.toString(transcriptLen));
// System.out.println("libSize: " + Arrays.toString(libSize));
Listener listener = new Listener();
try {
algorithm = framework.getAlgorithmFactory().getAlgorithm("DEGSEQ");
algorithm.addAlgorithmListener(listener);
this.progress = new Progress(framework.getFrame(), "Running DEGseq ...", listener);
this.progress.show();
AlgorithmData data = new AlgorithmData();
data.addIntMatrix("experiment", countMatrix);
data.addParam("dataDesign", String.valueOf(dataDesign));
data.addIntArray("group_assignments", twoClassGrps);
data.addParam("grp1ColIndStr", grp1ColIndStr);
data.addParam("grp2ColIndStr", grp2ColIndStr);
data.addIntArray("libSize", libSize);
data.addParam("numGenes", String.valueOf(numGenes));
data.addParam("numExps", String.valueOf(samplesUsed));
data.addParam("grp1", String.valueOf(grp1));
data.addParam("grp2", String.valueOf(grp2));
data.addParam("infMethod", infMethod);
data.addStringArray("geneLabels", geneLabels.toArray(new String[geneLabels.size()]));
data.addStringArray("sampleLabels", sampleLabels.toArray(new String[sampleLabels.size()]));
// run algorithm
long start = System.currentTimeMillis();
AlgorithmData result = algorithm.execute(data);
long time = System.currentTimeMillis() - start;
// getting the results
resultMatrix = result.getMatrix("result");
resultRowNames = result.getStringArray("rownames");
// System.out.println(Arrays.toString(resultRowNames));
// Process results
createHeaderNames();
createAuxData();
createResultClusters();
GeneralInfo info = new GeneralInfo();
info.time = time;
System.out.println("Creating Viewers for DEGSeq...");
return createResultTree(info);
} finally {
if (algorithm != null) {
algorithm.removeAlgorithmListener(listener);
}
if (progress != null) {
progress.dispose();
}
}
}
/** Initialize the algorithm's parameters and execute it. */
public DefaultMutableTreeNode execute(IFramework framework) throws AlgorithmException {
this.framework = framework;
this.experiment = framework.getData().getExperiment();
this.iData = framework.getData();
Listener listener = new Listener();
try {
DAMInitDialog damInitDialog = new DAMInitDialog(framework.getFrame(), true);
if (damInitDialog.showModal() != JOptionPane.OK_OPTION) {
return null;
}
classifyGenes = damInitDialog.isEvaluateGenesSelected();
algorithmSelection = damInitDialog.getAssessmentSelection();
isPDA = damInitDialog.isPDASelected();
numberOfClasses = damInitDialog.getNumClasses();
kValue = damInitDialog.getKValue();
alpha = damInitDialog.getAlphaValue();
preSelectGenes = !(damInitDialog.getSkipGeneSelectionValue());
DAMClassificationEditor damClassEditor =
new DAMClassificationEditor(framework, classifyGenes, numberOfClasses);
damClassEditor.setVisible(true);
while (!damClassEditor.isNextPressed()) {
if (damClassEditor.isCancelPressed()) {
return null;
} else {
continue;
}
}
AlgorithmData data = new AlgorithmData();
boolean useGenes = damInitDialog.isEvaluateGenesSelected();
if (useGenes) {
mode = 1;
data.addParam("dam-mode", "1");
} else {
mode = 3;
data.addParam("dam-mode", "3");
}
classificationVector = damClassEditor.getClassification();
trainingIndices = new int[classificationVector[0].size()];
classes = new int[classificationVector[1].size()];
testIndices = new int[classificationVector[2].size()];
for (int i = 0; i < trainingIndices.length; i++) {
trainingIndices[i] = ((Integer) (classificationVector[0].get(i))).intValue();
classes[i] = ((Integer) (classificationVector[1].get(i))).intValue();
}
for (int i = 0; i < testIndices.length; i++) {
testIndices[i] = ((Integer) (classificationVector[2].get(i))).intValue();
}
algorithm = framework.getAlgorithmFactory().getAlgorithm("DAM");
algorithm.addAlgorithmListener(listener);
logger = new Logger(framework.getFrame(), "DAM Log Window", listener);
logger.show();
logger.append("Starting DAM calculation\n");
FloatMatrix Cov;
Experiment experiment = framework.getData().getExperiment();
if (classifyGenes) {
// Problem here: if the program has a gene cluster (maybe also if it
// has an experiment cluster) the matrix returned is null.
FloatMatrix temp;
temp = (experiment.getMatrix()).transpose();
// System.out.println("floatmatrix size: " + temp.m + ", " + temp.n);
data.addMatrix("experiment", temp);
} else {
data.addMatrix("experiment", experiment.getMatrix());
}
data.addParam("distance-factor", String.valueOf(1.0f));
IDistanceMenu menu = framework.getDistanceMenu();
data.addParam("distance-absolute", String.valueOf(menu.isAbsoluteDistance()));
data.addParam("algorithmSelection", String.valueOf(algorithmSelection));
data.addParam("isPDA", String.valueOf(isPDA));
data.addParam("preSelectGenes", String.valueOf(preSelectGenes));
data.addParam("numberOfClasses", String.valueOf(numberOfClasses));
data.addParam("kValue", String.valueOf(kValue));
data.addParam("alpha", String.valueOf(alpha));
data.addIntArray("trainingIndices", trainingIndices);
data.addIntArray("classes", classes);
data.addIntArray("testIndices", testIndices);
int function = menu.getDistanceFunction();
if (function == Algorithm.DEFAULT) {
function = Algorithm.COVARIANCE;
}
data.addParam("distance-function", String.valueOf(function));
data.addParam("dam-mode", String.valueOf(mode));
AlgorithmData result = null;
DefaultMutableTreeNode node = null;
long start = System.currentTimeMillis();
switch (mode) {
case 1: // Spots
data.addParam("distance-function", String.valueOf(function));
result = algorithm.execute(data);
matrixS = result.getMatrix("S");
matrix3D = result.getMatrix("matrix3D");
usedGeneIndices = result.getIntArray("usedGeneIndices");
unusedGeneIndices = result.getIntArray("unusedGeneIndices");
node = new DefaultMutableTreeNode("DAM - genes");
break;
case 3: // Experiments
result = algorithm.execute(data);
matrixS = result.getMatrix("S");
matrix3D = result.getMatrix("matrix3D");
usedGeneIndices = result.getIntArray("usedGeneIndices");
unusedGeneIndices = result.getIntArray("unusedGeneIndices");
/*
if (preSelectGenes) {
System.out.println("DAMGUI.java: usedGeneIndices size: " + usedGeneIndices.length);
for(int i=0; i< usedGeneIndices.length; i++) {
System.out.print(usedGeneIndices[i] + ", ");
}
System.out.println(" ");
System.out.println(" ");
System.out.println("DAMGUI.java: unusedGeneIndices size: " + unusedGeneIndices.length);
for(int i=0; i< unusedGeneIndices.length; i++) {
System.out.print(unusedGeneIndices[i] + ", ");
}
System.out.println(" ");
System.out.println(" ");
}
*/
node = new DefaultMutableTreeNode("DAM - samples");
break;
default:
break;
}
Cluster result_cluster = result.getCluster("cluster");
NodeList nodeList = result_cluster.getNodeList();
int k = numberOfClasses * 3;
this.clusters = new int[k][];
// System.out.println(" ");
for (int i = 0; i < k; i++) {
clusters[i] = nodeList.getNode(i).getFeaturesIndexes();
}
Cluster gene_cluster = result.getCluster("geneCluster");
nodeList = gene_cluster.getNodeList();
this.geneClusters = new int[2][];
// System.out.println(" ");
for (int i = 0; i < 2; i++) {
geneClusters[i] = nodeList.getNode(i).getFeaturesIndexes();
}
this.means = result.getMatrix("clusters_means");
this.variances = result.getMatrix("clusters_variances");
this.means_used = result.getMatrix("clusters_means_used");
this.variances_used = result.getMatrix("clusters_variances_used");
this.means_unused = result.getMatrix("clusters_means_unused");
this.variances_unused = result.getMatrix("clusters_variances_unused");
columns = new int[(experiment.getMatrix()).getColumnDimension()];
for (int i = 0; i < columns.length; i++) {
columns[i] = i;
}
rows = new int[(experiment.getMatrix()).getRowDimension()];
for (int i = 0; i < rows.length; i++) {
rows[i] = i;
}
if (classifyGenes) {
usedExperiment =
new Experiment(
(experiment.getMatrix()).getMatrix(rows, usedGeneIndices), usedGeneIndices, rows);
unusedExperiment =
new Experiment(
(experiment.getMatrix()).getMatrix(rows, unusedGeneIndices),
unusedGeneIndices,
rows);
} else {
usedExperiment =
new Experiment(
(experiment.getMatrix()).getMatrix(usedGeneIndices, columns),
columns,
usedGeneIndices);
unusedExperiment =
new Experiment(
(experiment.getMatrix()).getMatrix(unusedGeneIndices, columns),
columns,
unusedGeneIndices);
}
/*
System.out.println("DAMGUI.java - means: " + means.getRowDimension() + " X " + means.getColumnDimension());
System.out.println("DAMGUI.java - variances: " + variances.getRowDimension() + " X " + variances.getColumnDimension());
System.out.println("DAMGUI.java - matrix3D " + matrix3D.getRowDimension() + " X " + matrix3D.getColumnDimension());
for(int i=0; i< matrix3D.getRowDimension(); i++) {
for(int j=0; j< matrix3D.getColumnDimension(); j++) {
System.out.print(matrix3D.get(i, j) + ", ");
}
System.out.println(" ");
}
*/
logger.append("Creating the result viewers\n");
long time = System.currentTimeMillis() - start;
if (algorithmSelection == A3) // only classification
addClassificationResultNodes(
framework.getFrame(), node, time, menu.getFunctionName(function), experiment);
else
addValidationResultNodes(
framework.getFrame(), node, time, menu.getFunctionName(function), experiment);
return node;
} finally {
if (algorithm != null) {
algorithm.removeAlgorithmListener(listener);
}
if (logger != null) {
logger.dispose();
}
}
}
