/*
* The MIT License (MIT)
*
* Copyright (c) 2011-2016 Broad Institute, Aiden Lab
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
package juicebox.tools.utils.juicer.apa;
import juicebox.data.HiCFileTools;
import juicebox.data.MatrixZoomData;
import juicebox.tools.utils.common.MatrixTools;
import juicebox.tools.utils.common.StatPercentile;
import juicebox.track.feature.Feature2D;
import juicebox.windowui.NormalizationType;
import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import java.io.*;
import java.util.ArrayList;
import java.util.List;
/**
* Created by Muhammad Shamim on 1/21/15.
*/
public class APAUtils {
/**
* @param filename
* @param matrix
*/
public static void saveMeasures(String filename, RealMatrix matrix, int currentRegionWidth) {
Writer writer = null;
APARegionStatistics apaStats = new APARegionStatistics(matrix, currentRegionWidth);
try {
writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(filename), "utf-8"));
writer.write("P2M" + '\t' + apaStats.getPeak2mean() + '\n');
writer.write("P2UL" + '\t' + apaStats.getPeak2UL() + '\n');
writer.write("P2UR" + '\t' + apaStats.getPeak2UR() + '\n');
writer.write("P2LL" + '\t' + apaStats.getPeak2LL() + '\n');
writer.write("P2LR" + '\t' + apaStats.getPeak2LR() + '\n');
writer.write("ZscoreLL" + '\t' + apaStats.getZscoreLL());
} catch (IOException ex) {
ex.printStackTrace();
} finally {
try {
if (writer != null)
writer.close();
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
public static void saveListText(String filename, List<Double> array) {
Writer writer = null;
try {
writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(filename), "utf-8"));
for (double val : array) {
writer.write(val + " ");
}
writer.write("\n");
} catch (IOException ex) {
ex.printStackTrace();
} finally {
try {
if (writer != null)
writer.close();
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
public static RealMatrix standardNormalization(RealMatrix matrix) {
return matrix.copy().scalarMultiply(1. / Math.max(1., MatrixTools.mean(matrix)));
}
public static RealMatrix centerNormalization(RealMatrix matrix) {
int center = matrix.getRowDimension() / 2;
double centerVal = matrix.getEntry(center, center);
if (centerVal == 0) {
centerVal = MatrixTools.minimumPositive(matrix);
if (centerVal == 0)
centerVal = 1;
}
return matrix.copy().scalarMultiply(1. / centerVal);
}
public static double peakEnhancement(RealMatrix matrix) {
int rows = matrix.getRowDimension();
int center = rows / 2;
double centerVal = matrix.getEntry(center, center);
double remainingSum = APARegionStatistics.sum(matrix.getData()) - centerVal;
double remainingAverage = remainingSum / (rows * rows - 1);
return centerVal / remainingAverage;
}
/**
* @param data
* @return
*/
public static RealMatrix rankPercentile(RealMatrix data) {
int n = data.getColumnDimension();
StatPercentile percentile = new StatPercentile(MatrixTools.flattenedRowMajorOrderMatrix(data));
RealMatrix matrix = new Array2DRowRealMatrix(n, n);
for (int r = 0; r < n; r++) {
for (int c = 0; c < n; c++) {
double currValue = data.getEntry(r, c);
if (currValue == 0) {
matrix.setEntry(r, c, 0);
} else {
matrix.setEntry(r, c, percentile.evaluate(currValue));
}
//matrix.setEntry(r, c, percentile.evaluate());
}
}
return matrix;
}
/**
* Size filtering of loops
*
* @param features
* @param minPeakDist
* @param maxPeakDist
* @return
*/
public static ArrayList<Feature2D> filterFeaturesBySize(List<Feature2D> features,
double minPeakDist, double maxPeakDist, int resolution) {
ArrayList<Feature2D> sizeFilteredFeatures = new ArrayList<Feature2D>();
for (Feature2D feature : features) {
double xMidPt = feature.getMidPt1();
double yMidPt = feature.getMidPt2();
int dist = (int) Math.round(Math.abs(xMidPt - yMidPt) / resolution);
if (dist >= minPeakDist) {
if (dist <= maxPeakDist) {
sizeFilteredFeatures.add(feature);
}
}
}
return new ArrayList<Feature2D>(sizeFilteredFeatures);
}
public static RealMatrix extractLocalizedData(MatrixZoomData zd, Feature2D loop,
int L, int resolution, int window, NormalizationType norm) throws IOException {
int loopX = loop.getMidPt1() / resolution;
int loopY = loop.getMidPt2() / resolution;
int binXStart = loopX - window;
int binXEnd = loopX + (window + 1);
int binYStart = loopY - window;
int binYEnd = loopY + (window + 1);
return HiCFileTools.extractLocalBoundedRegion(zd, binXStart, binXEnd, binYStart, binYEnd, L, L, norm);
}
}
