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TASSEL 6 is a software package to evaluate traits association. Feature Tables are at the heart of the package where, a feature is a range of positions or a single position. Row in the that table are taxon.
// DistanceMatrixUtils.java
//
// (c) 1999-2001 PAL Development Core Team
//
// This package may be distributed under the
// terms of the Lesser GNU General Public License (LGPL)
package net.maizegenetics.taxa.distance;
import net.maizegenetics.taxa.TaxaList;
import net.maizegenetics.taxa.TaxaListBuilder;
import net.maizegenetics.taxa.Taxon;
import net.maizegenetics.util.BitSet;
import net.maizegenetics.util.BitUtil;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* Utility functions for distance matrices
*
* @author Alexei Drummond
* @author Terry Casstevens
*/
public class DistanceMatrixUtils {
private DistanceMatrixUtils() {
// utility class
}
/**
* Get Genetic Relationship Matrix (grm) file names.
*
* @param base filename base
*
* @return array of file names. Index 0 is the id filename (.grm.id); Index 1 is the binary matrix (.grm.bin); Index
* 2 is the binary counts (.grm.N.bin); Index 3 is the raw (text) matrix (.grm.raw); Index 4 is the text matrix
* (.txt)
*/
public static String[] getGRMFilenames(String base) {
String[] result = new String[5];
String filename = base.toLowerCase();
if (filename.endsWith(".txt")) {
int txtIndex = filename.lastIndexOf(".txt");
String temp = base.substring(0, txtIndex);
result[0] = temp + ".grm.id";
result[1] = temp + ".grm.bin";
result[2] = temp + ".grm.N.bin";
result[3] = temp + ".grm.raw";
result[4] = filename;
return result;
}
int grmIndex = filename.lastIndexOf(".grm");
if (grmIndex == -1) {
result[0] = base + ".grm.id";
result[1] = base + ".grm.bin";
result[2] = base + ".grm.N.bin";
result[3] = base + ".grm.raw";
result[4] = base + ".txt";
} else {
String temp = base.substring(0, grmIndex);
result[0] = temp + ".grm.id";
result[1] = temp + ".grm.bin";
result[2] = temp + ".grm.N.bin";
result[3] = temp + ".grm.raw";
result[4] = temp + ".txt";
}
return result;
}
/**
* Get DARwin file names.
*
* @param base filename base
*
* @return array of file names. Index 0 is the id filename (.don); Index 1 is the dissimilarity matrix (.dis)
*/
public static String[] getDARwinFilenames(String base) {
String[] result = new String[2];
int index = base.lastIndexOf(".");
if (index == -1) {
result[0] = base + ".don";
result[1] = base + ".dis";
return result;
} else {
String temp = base.substring(0, index);
result[0] = temp + ".don";
result[1] = temp + ".dis";
return result;
}
}
/**
* compute squared distance to second distance matrix. If both matrices have
* the same size it is assumed that the order of the taxa is identical.
*/
public static double squaredDistance(DistanceMatrix mat1, DistanceMatrix mat2, boolean weighted) {
boolean aliasNeeded = false;
if (mat1.getSize() != mat2.getSize()) {
aliasNeeded = true;
}
int[] alias = null;
if (aliasNeeded) {
if (mat1.getSize() > mat2.getSize()) {
//swap so mat1 is the smaller of the two
DistanceMatrix temp = mat2;
mat2 = mat1;
mat1 = temp;
}
alias = new int[mat1.getSize()];
for (int i = 0; i < alias.length; i++) {
alias[i] = mat2.whichIdNumber(mat1.getTaxon(i).getName());
}
} else {
alias = new int[mat1.getSize()];
for (int i = 0; i < alias.length; i++) {
alias[i] = i;
}
}
double sum = 0;
int ai;
final double[][] mat1Distance = mat1.getDistances();
final double[][] mat2Distance = mat2.getDistances();
for (int i = 0; i < mat1.getSize() - 1; i++) {
ai = alias[i];
for (int j = i + 1; j < mat1.getSize(); j++) {
double diff = mat1Distance[i][j] - mat2Distance[ai][alias[j]];
double weight;
if (weighted) {
// Fitch-Margoliash weight
// (variances proportional to distances)
weight = 1.0 / (mat1Distance[i][j] * mat2Distance[ai][alias[j]]);
} else {
// Cavalli-Sforza-Edwards weight
// (homogeneity of variances)
weight = 1.0;
}
sum += weight * diff * diff;
}
}
return 2.0 * sum; // we counted only half the matrix
}
/**
* Returns a distance matrix with the specified taxa removed.
*/
public static DistanceMatrix minus(DistanceMatrix parent, int taxaToRemove) {
int size = parent.numberOfTaxa() - 1;
double[][] distances = new double[size][size];
Taxon[] ids = new Taxon[size];
int counti = 0, countj = 0;
for (int i = 0; i < size; i++) {
if (counti == taxaToRemove) {
counti += 1;
}
ids[i] = parent.getTaxon(counti);
countj = 0;
final double[][] parentDistance = parent.getDistances();
for (int j = 0; j < size; j++) {
if (countj == taxaToRemove) {
countj += 1;
}
distances[i][j] = parentDistance[counti][countj];
countj += 1;
}
counti += 1;
}
TaxaList tl = new TaxaListBuilder().addAll(ids).build();
DistanceMatrix smaller = new DistanceMatrix(distances, tl);
return smaller;
}
/**
* @param parent the DistanceMatrix from which to extract a subset
* @param taxaToKeep an index of the taxa to keep
*
* @return A DistanceMatrix with all the taxa that are in both parent and taxaToKeep in the same order as taxaToKeep
*/
public static DistanceMatrix keepTaxa(DistanceMatrix parent, int[] taxaToKeep) {
int ntaxa = taxaToKeep.length;
double[][] newDistances = new double[ntaxa][ntaxa];
for (int r = 0; r < ntaxa; r++) {
for (int c = 0; c < ntaxa; c++) {
newDistances[r][c] = parent.getDistance(taxaToKeep[r], taxaToKeep[c]);
}
}
TaxaListBuilder taxaBuilder = new TaxaListBuilder();
for (int ndx : taxaToKeep) {
taxaBuilder.add(parent.getTaxon(ndx));
}
TaxaList taxaListToKeep = taxaBuilder.build();
return new DistanceMatrix(newDistances, taxaListToKeep);
}
/**
* @param parent the DistanceMatrix from which to extract a subset
* @param taxaToKeep a TaxaList of taxa to be kept
*
* @return a DistanceMatrix that contains only the taxa that are in both taxaToKeep and parent. The taxa will be in
* the same order as taxaToKeep.
*/
public static DistanceMatrix keepTaxa(DistanceMatrix parent, TaxaList taxaToKeep) {
int[] keepIndex = taxaToKeep.stream()
.mapToInt(t -> parent.whichIdNumber(t))
.filter(i -> i > -1)
.toArray();
return keepTaxa(parent, keepIndex);
}
public static DistanceMatrix clusterBySmallestDistance(DistanceMatrix orig) {
TaxaList taxa = orig.getTaxaList();
int numTaxa = taxa.numberOfTaxa();
TaxaPairLowestDistance[] lowValues = new TaxaPairLowestDistance[numTaxa];
for (int t = 0; t < numTaxa; t++) {
lowValues[t] = new TaxaPairLowestDistance(t);
}
for (int x = 0; x < numTaxa; x++) {
for (int y = x + 1; y < numTaxa; y++) {
float value = orig.getDistance(x, y);
if (!Float.isNaN(value)) {
if (lowValues[x].myLowValue > value) {
lowValues[x].myLowValue = value;
lowValues[x].myTaxon2 = y;
}
if (lowValues[y].myLowValue > value) {
lowValues[y].myLowValue = value;
lowValues[y].myTaxon2 = x;
}
}
}
}
Arrays.sort(lowValues);
List> clusters = new ArrayList<>();
List[] whichCluster = new ArrayList[numTaxa];
List unknownList = new ArrayList<>();
for (int t = 0; t < numTaxa; t++) {
int taxon1 = lowValues[t].myTaxon1;
int taxon2 = lowValues[t].myTaxon2;
if (taxon2 == -1) {
unknownList.add(taxon1);
} else if (whichCluster[taxon1] == null && whichCluster[taxon2] == null) {
List temp = new ArrayList<>();
temp.add(taxon1);
temp.add(taxon2);
clusters.add(temp);
whichCluster[taxon1] = temp;
whichCluster[taxon2] = temp;
} else if (whichCluster[taxon1] == null) {
whichCluster[taxon1] = whichCluster[taxon2];
whichCluster[taxon1].add(taxon1);
} else if (whichCluster[taxon2] == null) {
whichCluster[taxon2] = whichCluster[taxon1];
whichCluster[taxon2].add(taxon2);
}
}
clusters.add(unknownList);
DistanceMatrixBuilder builder = DistanceMatrixBuilder.getInstance(numTaxa);
int count = 0;
for (List current : clusters) {
int currentNumTaxa = current.size();
for (int taxon = 0; taxon < currentNumTaxa; taxon++) {
builder.addTaxon(taxa.get(current.get(taxon)));
for (int x = taxon; x < currentNumTaxa; x++) {
builder.set(x + count, taxon + count, orig.getDistance(current.get(taxon), current.get(x)));
}
}
count += currentNumTaxa;
}
return builder.build();
}
private static class TaxaPairLowestDistance implements Comparable {
private final int myTaxon1;
private int myTaxon2;
private float myLowValue = Float.POSITIVE_INFINITY;
public TaxaPairLowestDistance(int taxon1) {
myTaxon1 = taxon1;
myTaxon2 = -1;
myLowValue = Float.POSITIVE_INFINITY;
}
@Override
public int compareTo(TaxaPairLowestDistance o) {
if (myLowValue < o.myLowValue) {
return -1;
} else if (myLowValue > o.myLowValue) {
return 1;
} else {
return 0;
}
}
}
/**
* Calculates the IBS distance between two taxa with bitsets for for major
* and minor allele
*
* @param iMajor
* @param iMinor
* @param jMajor
* @param jMinor
*
* @return
*/
public static double getIBSDistance(long[] iMajor, long[] iMinor, long[] jMajor, long[] jMinor) {
int sameCnt = 0, diffCnt = 0, hetCnt = 0;
for (int x = 0; x < iMajor.length; x++) {
long same = (iMajor[x] & jMajor[x]) | (iMinor[x] & jMinor[x]);
long diff = (iMajor[x] & jMinor[x]) | (iMinor[x] & jMajor[x]);
long hets = same & diff;
sameCnt += BitUtil.pop(same);
diffCnt += BitUtil.pop(diff);
hetCnt += BitUtil.pop(hets);
}
double identity = (double) (sameCnt + (hetCnt / 2)) / (double) (sameCnt + diffCnt + hetCnt);
double dist = 1 - identity;
return dist;
}
public static double getIBSDistance(BitSet iMajor, BitSet iMinor, BitSet jMajor, BitSet jMinor) {
return getIBSDistance(iMajor.getBits(), iMinor.getBits(), jMajor.getBits(), jMinor.getBits());
}
}