public void executeFoldChange() {
int[] cols = dataset.getColumnIndexesByVariableValue(className, classValues.get(0));
DoubleMatrix sample1 = dataset.getSubMatrixByColumns(cols);
cols = dataset.getColumnIndexesByVariableValue(className, classValues.get(1));
DoubleMatrix sample2 = dataset.getSubMatrixByColumns(cols);
try {
Dataset subDataset = dataset.getSubDataset(className, classValues);
FoldChangeTest foldChange = new FoldChangeTest();
double[] logRes = foldChange.logFoldChange(sample1, sample2);
double[] diffRes = foldChange.diffFoldChange(sample1, sample2);
updateJobStatus("80", "saving results");
setFoldChangeResults(subDataset, "log", "Log", logRes, className);
setFoldChangeResults(subDataset, "diff", "Diff", diffRes, className);
// String test, testLabel;
// DataFrame dataFrame;
// FeatureData featureData;
// File file;
//
// int[] columnOrder;
// int[] rowOrder;
// Canvas heatmap;
// String heatmapFilename;
//
// // log fold change
// //
// test = "log";
// testLabel = "Log";
//
// columnOrder =
// ListUtils.order(subDataset.getVariables().getByName(className).getValues());
// rowOrder = ListUtils.order(ArrayUtils.toList(logRes), true);
//
// dataFrame = new DataFrame(subDataset.getFeatureNames().size(), 0);
// dataFrame.addColumn(test, ListUtils.ordered(ArrayUtils.toStringList(logRes), rowOrder));
// dataFrame.setRowNames(ListUtils.ordered(subDataset.getFeatureNames(), rowOrder));
// featureData = new FeatureData(dataFrame);
// file = new File(outdir + "/" + test + "_foldchange.txt");
// IOUtils.write(file, dataFrame.toString(true, true));
// //featureData.save(file);
// if ( file.exists() ) {
// result.addOutputItem(new Item(test + "_foldchange", file.getName(), testLabel + "
// fold-change output file", TYPE.FILE, StringUtils.toList("TABLE," + test.toUpperCase() +
// "_FOLD_CHANGE_TABLE", ","), new HashMap<String, String>(2), testLabel + "
// fold-change.Output files"));
// }
//
// // generating heatmap
// //
// heatmap = DiffExpressionUtils.generateHeatmap(subDataset, className, columnOrder,
// rowOrder, test + " fold-change", logRes, null, null);
// heatmapFilename = getOutdir() + "/" + test + "_heatmap.png";
// try {
// heatmap.save(heatmapFilename);
// if ( new File(heatmapFilename).exists() ) {
// result.addOutputItem(new Item(test + "_heatmap", test + "_heatmap.png", testLabel + ".
// fold-change heatmap with all terms", TYPE.IMAGE, new ArrayList<String>(2), new
// HashMap<String, String>(2), testLabel + " fold-change.Heatmap image"));
// }
// } catch (IOException e) {
// printError("ioexception_executet_classcomparison", "error generating heatmap",
// e.toString(), e);
// }
//
//
// test = "diff";
// testLabel = "Diff";
//
// columnOrder =
// ListUtils.order(subDataset.getVariables().getByName(className).getValues());
// rowOrder = ListUtils.order(ArrayUtils.toList(logRes), true);
//
// dataFrame = new DataFrame(subDataset.getFeatureNames().size(), 0);
// dataFrame.addColumn(test, ListUtils.ordered(ArrayUtils.toStringList(diffRes), rowOrder));
// dataFrame.setRowNames(ListUtils.ordered(subDataset.getFeatureNames(), rowOrder));
// featureData = new FeatureData(dataFrame);
// file = new File(outdir + "/" + test + "_foldchange.txt");
// IOUtils.write(file, dataFrame.toString(true, true));
// //featureData.save(file);
// if ( file.exists() ) {
// result.addOutputItem(new Item(test + "_foldchange", file.getName(), testLabel + "
// fold-change output file", TYPE.FILE, StringUtils.toList("TABLE," + test.toUpperCase() +
// "_FOLD_CHANGE_TABLE", ","), new HashMap<String, String>(2), testLabel + "
// fold-change.Output files"));
// }
//
// // generating heatmap
// //
// heatmap = DiffExpressionUtils.generateHeatmap(subDataset, className, columnOrder,
// rowOrder, test + " fold-change", diffRes, null, null);
// heatmapFilename = getOutdir() + "/" + test + "_heatmap.png";
// try {
// heatmap.save(heatmapFilename);
// if ( new File(heatmapFilename).exists() ) {
// result.addOutputItem(new Item(test + "_heatmap", test + "_heatmap.png", testLabel + ".
// fold-change heatmap with all terms", TYPE.IMAGE, new ArrayList<String>(2), new
// HashMap<String, String>(2), testLabel + " fold-change.Heatmap image"));
// }
// } catch (IOException e) {
// printError("ioexception_executet_classcomparison", "error generating heatmap",
// e.toString(), e);
// }
} catch (Exception e) {
e.printStackTrace();
abort(
"exception_executefoldchange_classcomparison",
"error running fold-change",
"error running fold-change: " + e.getMessage(),
"error running fold-change: " + e.getMessage());
}
}
public void executeAnova() {
DoubleMatrix matrix = null;
List<String> vars = new ArrayList<String>();
List<Integer> indices = new ArrayList<Integer>();
List<String> values = dataset.getVariables().getByName(className).getValues();
if (values.size() == classValues.size()) {
matrix = dataset.getDoubleMatrix();
vars = values;
} else {
for (int i = 0; i < values.size(); i++) {
if (classValues.contains(values.get(i))) {
indices.add(i);
vars.add(values.get(i));
}
}
matrix = dataset.getSubMatrixByColumns(ListUtils.toIntArray(indices));
}
try {
Dataset subDataset = dataset.getSubDataset(className, classValues);
// apply test and multiple test correction according
//
AnovaTest anova = new AnovaTest(matrix, vars);
TestResultList<AnovaTestResult> res = anova.compute();
DiffExpressionUtils.multipleTestCorrection(res, correction);
// create output file
//
int[] columnOrder =
ListUtils.order(subDataset.getVariables().getByName(className).getValues());
int[] rowOrder = ListUtils.order(ArrayUtils.toList(res.getStatistics()), true);
DataFrame dataFrame = new DataFrame(subDataset.getFeatureNames().size(), 0);
dataFrame.addColumn(
"statistic",
ListUtils.toStringList(
ListUtils.ordered(ArrayUtils.toList(res.getStatistics()), rowOrder)));
dataFrame.addColumn(
"p-value",
ListUtils.toStringList(ListUtils.ordered(ArrayUtils.toList(res.getPValues()), rowOrder)));
dataFrame.addColumn(
"adj. p-value",
ListUtils.toStringList(
ListUtils.ordered(ArrayUtils.toList(res.getAdjPValues()), rowOrder)));
dataFrame.setRowNames(ListUtils.ordered(subDataset.getFeatureNames(), rowOrder));
FeatureData featureData = new FeatureData(dataFrame);
File file = new File(outdir + "/" + test + ".txt");
featureData.save(file);
if (file.exists()) {
result.addOutputItem(
new Item(
test + "file",
file.getName(),
"Anova output file",
TYPE.FILE,
new ArrayList<String>(2),
new HashMap<String, String>(2),
"Anova output files"));
}
// getting significative genes
//
DiffExpressionUtils.addSignificativeResults(
subDataset,
test,
"statistic",
res.getStatistics(),
"adj. p-value",
res.getAdjPValues(),
"p-value",
res.getPValues(),
null,
null,
null,
null,
className,
columnOrder,
pValue,
maxDisplay,
this);
DiffExpressionUtils.createFatiScanRedirection(dataFrame, test, "statistic", result, outdir);
} catch (Exception e) {
e.printStackTrace();
abort(
"exception_executeanova_classcomparison",
"error running anova",
"error running anova: " + e.getMessage(),
"error running anova: " + e.getMessage());
}
}
public void executeT() {
int[] cols = dataset.getColumnIndexesByVariableValue(className, classValues.get(0));
DoubleMatrix sample1 = dataset.getSubMatrixByColumns(cols);
cols = dataset.getColumnIndexesByVariableValue(className, classValues.get(1));
DoubleMatrix sample2 = dataset.getSubMatrixByColumns(cols);
try {
Dataset subDataset = dataset.getSubDataset(className, classValues);
// apply test and multiple test correction according
//
TTest tTest = new TTest();
TestResultList<TTestResult> res = tTest.tTest(sample1, sample2);
DiffExpressionUtils.multipleTestCorrection(res, correction);
// create output file
//
int[] columnOrder =
ListUtils.order(subDataset.getVariables().getByName(className).getValues());
int[] rowOrder = ListUtils.order(ArrayUtils.toList(res.getStatistics()), true);
DataFrame dataFrame = new DataFrame(subDataset.getFeatureNames().size(), 0);
dataFrame.addColumn(
"statistic",
ListUtils.toStringList(
ListUtils.ordered(ArrayUtils.toList(res.getStatistics()), rowOrder)));
dataFrame.addColumn(
"p-value",
ListUtils.toStringList(ListUtils.ordered(ArrayUtils.toList(res.getPValues()), rowOrder)));
dataFrame.addColumn(
"adj. p-value",
ListUtils.toStringList(
ListUtils.ordered(ArrayUtils.toList(res.getAdjPValues()), rowOrder)));
dataFrame.setRowNames(ListUtils.ordered(subDataset.getFeatureNames(), rowOrder));
FeatureData featureData = new FeatureData(dataFrame);
File file = new File(outdir + "/t.txt");
featureData.save(file);
if (file.exists()) {
result.addOutputItem(
new Item(
"tfile",
file.getName(),
"T-test output file",
TYPE.FILE,
new ArrayList<String>(),
new HashMap<String, String>(),
"T-test output files"));
}
// getting significative genes
//
DiffExpressionUtils.addSignificativeResults(
subDataset,
test,
"statistic",
res.getStatistics(),
"adj. p-value",
res.getAdjPValues(),
"p-value",
res.getPValues(),
null,
null,
null,
null,
className,
columnOrder,
pValue,
maxDisplay,
this);
DiffExpressionUtils.createFatiScanRedirection(dataFrame, test, "statistic", result, outdir);
} catch (Exception e) {
e.printStackTrace();
abort("exception_executet_classcomparison", "ERROR", "Error running t-test", "");
}
}