Author: shirleyh
Date: 2008-09-23 12:08:11 -0700 (Tue, 23 Sep 2008)
New Revision: 14944
Added:
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/CorrelateTask.java
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/SpearmanRank.java
Log:
New files for Expression Correlation Plugin to work with Cytoscape 2.6.0
Added:
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/CorrelateTask.java
===================================================================
---
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/CorrelateTask.java
2008-09-23 19:07:05 UTC (rev 14943)
+++
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/CorrelateTask.java
2008-09-23 19:08:11 UTC (rev 14944)
@@ -0,0 +1,215 @@
+package org.mskcc.csplugins.ExpressionCorrelation;
+
+import cytoscape.task.Task;
+import cytoscape.task.TaskMonitor;
+import cytoscape.Cytoscape;
+import cytoscape.CyNetwork;
+
+/**
+ * Copyright (c) 2007
+ * *
+ * * Code written by: Shirley Hui
+ * * Authors: Gary Bader, Elena Potylitsine, Chris Sander, Weston Whitaker
+ * *
+ * * This library is free software; you can redistribute it and/or modify it
+ * * under the terms of the GNU Lesser General Public License as published
+ * * by the Free Software Foundation; either version 2.1 of the License, or
+ * * any later version.
+ * *
+ * * This library is distributed in the hope that it will be useful, but
+ * * WITHOUT ANY WARRANTY, WITHOUT EVEN THE IMPLIED WARRANTY OF
+ * * MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. The software and
+ * * documentation provided hereunder is on an "as is" basis, and
+ * * Memorial Sloan-Kettering Cancer Center
+ * * has no obligations to provide maintenance, support,
+ * * updates, enhancements or modifications. In no event shall the
+ * * Memorial Sloan-Kettering Cancer Center
+ * * be liable to any party for direct, indirect, special,
+ * * incidental or consequential damages, including lost profits, arising
+ * * out of the use of this software and its documentation, even if
+ * * Memorial Sloan-Kettering Cancer Center
+ * * has been advised of the possibility of such damage. See
+ * * the GNU Lesser General Public License for more details.
+ * *
+ * * You should have received a copy of the GNU Lesser General Public License
+ * * along with this library; if not, write to the Free Software Foundation,
+ * * Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
+ * *
+ * * User: Shirley Hui
+ * * Date: Apr 4, 2007
+ * * Time: 3:35:11 April 7, 2004
+ * * Description: CorrelateTask
+ * *
+ * *
+ */
+
+
+/**
+ * This class represents a task that calls methods to calculate a correlation
network and/or corresponding histogram
+ */
+public class CorrelateTask implements Task {
+
+ private CorrelateSimilarityNetwork network;
+ private TaskMonitor taskMonitor = null;
+ private CorrelateHistogramWindow histogram = null;
+
+ boolean colDone = false; // True when the column network is
complete
+
+ /**
+ * Sets which event thread to run and time
+ * case 1: Run and time both the column and row networks
+ * case 2: Run and time the column network
+ * case 3: Run and time the column histogram
+ * case 4: Run and time the row network
+ * case 5: Run and time the row histogram
+ */
+ public int event = 0;
+
+ /**
+ * Constructor.
+ *
+ * @param event Indicates which event to run.
+ * @param network The network upon which to calculate correlations
+ */
+ public CorrelateTask(int event, CorrelateSimilarityNetwork network) {
+
+ this.network = network;
+ this.event = event;
+ }
+
+ /**
+ * Perform the correlation calculations on the network.
+ */
+ public void run() {
+ if (network.getTaskMonitor() == null) {
+ throw new IllegalStateException("Task Monitor is not set.");
+ }
+
+ switch (event) {
+ case 1:
+ colRun();
+ rowRun();
+ break;
+ case 2:
+ colRun();
+ break;
+ case 3:
+ colHistogram();
+ break;
+ case 4:
+ rowRun();
+ break;
+ case 5:
+ rowHistogram();
+ break;
+ }
+ }
+
+
+ private void cleanup(String networkId)
+ {
+ System.out.println("Removing network with id: " + networkId);
+ if (networkId != null)
+ {
+ CyNetwork destroyNetwork = Cytoscape.getNetwork(networkId);
+ Cytoscape.destroyNetwork( destroyNetwork );
+ }
+
+ System.out.println("Finished destroying Network");
+
+ }
+
+ /**
+ * Halts the Task: Not Currently Implemented.
+ */
+ public void halt() {
+ // Task can not currently be halted.
+ network.cancel();
+ System.out.println("User cancelled task.");
+ if (histogram!=null)
+ {
+ System.out.println("Disposing histogram dialog");
+ histogram.dispose();
+ histogram.setVisible(false);
+ }
+ }
+
+ /**
+ * Sets the Task Monitor.
+ *
+ * @param taskMonitor TaskMonitor Object.
+ */
+ public void setTaskMonitor(TaskMonitor taskMonitor) {
+ if (this.taskMonitor != null) {
+ throw new IllegalStateException("Task Monitor is already set.");
+ }
+ network.setTaskMonitor(taskMonitor);
+ }
+
+ /**
+ * Gets the Task Title.
+ *
+ * @return human readable task title.
+ */
+ public String getTitle() {
+ return new String("Performing correlation calculations");
+ }
+
+ /**
+ * The condition matrix calculation
+ */
+ public void colRun() {
+ if (Cytoscape.getExpressionData() != null) {
+ System.out.println("Starting colRun");
+
+ CyNetwork cyNetwork = network.calcCols();
+ String conditionNetworkId = cyNetwork.getIdentifier();
+ if (network.cancelled())
+ {
+ cleanup(conditionNetworkId);
+ }
+ System.out.println("Finished colRun");
+ }
+ }
+
+ /**
+ * The gene matrix calculation
+ */
+ public void rowRun() {
+
+ if (Cytoscape.getExpressionData() != null) {
+ System.out.println("Starting rowRun");
+ CyNetwork cyNetwork = network.calcRows();
+ String geneNetworkId = cyNetwork.getIdentifier();
+ if (network.cancelled())
+ {
+ cleanup(geneNetworkId);
+ }
+ System.out.println("Finished rowRun");
+ }
+ }
+
+ /**
+ * The condition histogram calculation
+ */
+ public void colHistogram() {
+ if (Cytoscape.getExpressionData() != null) {
+ System.out.println("Starting colHistogram");
+ histogram = new CorrelateHistogramWindow(Cytoscape.getDesktop(),
false, network); //not row histogram
+ histogram.pack();
+ histogram.setVisible(true);
+ }
+ }
+
+ /**
+ * The gene histogram calculation
+ */
+ public void rowHistogram() {
+ if (Cytoscape.getExpressionData() != null) {
+ System.out.println("Starting rowHistogram");
+ histogram = new CorrelateHistogramWindow(Cytoscape.getDesktop(),
true, network); //row histogram
+ histogram.pack();
+ histogram.setVisible(true);
+ }
+ }
+}
\ No newline at end of file
Added:
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/SpearmanRank.java
===================================================================
---
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/SpearmanRank.java
2008-09-23 19:07:05 UTC (rev 14943)
+++
csplugins/trunk/mskcc/summerstudents/ExpressionCorrelation/src/org/mskcc/csplugins/ExpressionCorrelation/SpearmanRank.java
2008-09-23 19:08:11 UTC (rev 14944)
@@ -0,0 +1,201 @@
+//
+// SpearmanRank.java
+//
+//
+// Created by Shirley Hui on 08/05/07.
+// Copyright 2007 __MyCompanyName__. All rights reserved.
+//
+package org.mskcc.csplugins.ExpressionCorrelation;
+
+import cern.colt.matrix.*;
+import cern.colt.matrix.linalg.Algebra;
+import cern.colt.matrix.impl.*;
+import cern.colt.matrix.doublealgo.*;
+import java.util.*;
+import cern.jet.math.Functions;
+import cern.colt.matrix.doublealgo.Statistic;
+
+public class SpearmanRank {
+
+ private List rankInfoList;
+ private DoubleMatrix2D distanceMatrix;
+
+ public SpearmanRank(DoubleMatrix2D data)
+ {
+ System.out.println("Initializing Spearman Rank...");
+ init(data);
+ }
+
+ // Perform quicksort on 2 d matrix v1, but sort wrt to first column
+ private DoubleMatrix2D sort(DoubleMatrix2D v1)
+ {
+ DoubleMatrix2D view = Sorting.quickSort.sort(v1,0);
+ return view;
+ }
+
+ private RankInfo rank(double[] x)
+ {
+ int j = 0;
+ int ji;
+ int jt;
+ double t;
+ int n = x.length;
+ double r;
+ double s = 0.0;
+ double[] w = new double[n];
+
+ for (int i = 0;i < n;i++)
+ {
+ w[i] = x[i];
+ }
+
+ while (j < n)
+ {
+ if (j == n-1)
+ break;
+ // not a tie
+ if (w[j+1] != w[j] && j < n)
+ {
+ w[j] = j;
+ j = j + 1;
+ } else
+ {
+ for (jt = j+ 1; jt < n && w[jt]==w[j];jt++);
+ r = 0.5*(j+jt-1);
+ for (ji=j;ji <=(jt-1);ji++) w[ji] = r;
+ t = jt-j;
+ s = s + t*t*t-1;
+ j = jt;
+ }
+ }
+ if (j==n-1) w[n-1] = n-1;
+
+ /*for (int i = 0; i < w.length;i++)
+ {
+ System.out.print( w[i] + ", ");
+ }
+ System.out.println();
+ */
+ return new RankInfo(w,s);
+ //return w;
+ }
+
+ private static DoubleMatrix2D addIndices(DoubleMatrix1D x)
+ {
+ int n = x.size();
+ // Create a 2D matrix. The first column is v1, the second
column is [1..v1.length]
+ DenseDoubleMatrix2D v = new DenseDoubleMatrix2D(n,2);
+
+ for (int i = 0; i < n; i++)
+ {
+ for (int j = 0;j < 2;j++)
+ {
+ double xValue = x.get(i);
+
+ if (j == 0)
+ {
+ //System.out.print(x.get(i));
+ v.setQuick(i, j, xValue);
+ }
+ else
+ v.setQuick(i,j,i);
+ }
+ }
+
+ //DoubleMatrix2D view =Sorting.quickSort.sort(v1,0);
+ //System.out.println(view);
+ return v;
+ }
+
+ private void init(DoubleMatrix2D rdata)
+ {
+ int n = rdata.rows();
+ rankInfoList = new ArrayList();
+ for (int i = 0; i < n;i++)
+ {
+ DoubleMatrix1D x = rdata.viewRow(i);
+ DoubleMatrix2D v1 = addIndices(x);
+ // Sort v1 wrt to first column
+ DoubleMatrix2D sortedV1 = sort(v1);
+ // v1 first column are the sorted data values
+ DoubleMatrix1D sortedColV1 = sortedV1.viewColumn(0);
+ // v1 second column are the original indices sorted
according to first column
+ DoubleMatrix1D sortedIndicesV1 = sortedV1.viewColumn(1);
+
+ double[] vv2 = sortedColV1.toArray();
+ RankInfo rr1 = rank(vv2);
+ rr1.setIndices(sortedIndicesV1.toArray());
+
+ rankInfoList.add(rr1);
+ }
+
+ System.out.println("rankInfoList contains: " + rankInfoList.size() + "
number of RankInfos");
+ Algebra A = new Algebra();
+ distanceMatrix =
Statistic.distance(A.transpose(rdata),Statistic.EUCLID);
+ System.out.println("The distance matrix is: " + distanceMatrix.rows()
+ " rows by " + distanceMatrix.columns() + "columns");
+ }
+
+ /**
+ * calculate the spearman correlation for two vectors d and d2
+ **/
+ public double corr(int xindex, int yindex)
+ {
+ if (rankInfoList.size()==0)
+ return -2.0;
+
+ RankInfo xrank = (RankInfo) rankInfoList.get(xindex);
+ RankInfo yrank = (RankInfo) rankInfoList.get(yindex);
+
+ double[] r1 = xrank.ranking;
+ double sf = xrank.s;
+ double[] r1index = xrank.sortedIndices;
+
+ double[] r2 = yrank.ranking;
+ double sg = yrank.s;
+ double[] r2index = yrank.sortedIndices;
+
+ // compute sum of square diffs
+ //DoubleMatrix1D d1 = new DenseDoubleMatrix1D(r1);
+ //DoubleMatrix1D d2 = new DenseDoubleMatrix1D(r2);
+
+ //double sumOfDiffSquared = d1.aggregate(d2, Functions.plus,
Functions.chain(Functions.square,Functions.minus));
+
+ int n = r1.length;
+ double d1 = 0.0;
+ /*
+ for (int j = 0; j <n;j++)
+ {
+ double diff = r1[(int)r1index[j]]-r2[(int)r2index[j]];
+ //double diff = 0.0;
+ double diffSquared = diff*diff;
+ d1 = d1 + diffSquared;
//Math.pow(r1[(int)r1index[j]]-r2[(int)r2index[j]],2);
+ } */
+ double en = (double)n;
+ double en3n = en*en*en-en;
+ /*double fac = (1.0-sf/en3n)*(1.0-sg/en3n);
+ double p = (1.0 - (6.0/en3n)*(d1+(sf+sg)/12.0))/Math.sqrt(fac);
+ */
+ double p = 1.0 - (6.0 * d1)/(en3n-n);
+ return p;
+ }
+
+ private class RankInfo
+ {
+ double[] ranking;
+ double s = 0.0;
+ double[] sortedIndices;
+
+ public RankInfo(double[] ranking, double s)
+ {
+ this.ranking = ranking;
+ this.s = s;
+ }
+
+ public void setIndices(double[] sortedIndices)
+ {
+ this.sortedIndices = sortedIndices;
+ }
+ }
+
+
+}
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