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Intermediate commit (debugging info). 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@309 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
kiran 2009-04-07 01:20:15 +00:00
parent b42d8df646
commit 9be978e006
3 changed files with 104 additions and 71 deletions
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129
java/src/org/broadinstitute/sting/playground/fourbasecaller/BasecallingBaseModel.java
View File

@ -21,39 +21,28 @@ import java.io.*;
* @author Kiran Garimella
*/
public class BasecallingBaseModel {
private double[][] counts;
private DoubleMatrix1D[][] runningChannelSums;
private DoubleMatrix2D[][] runningChannelProductSums;
private boolean readyToCall = false;
private DoubleMatrix1D[][] means;
private DoubleMatrix2D[][] inverseCovariances;
private double[][] norms;
private double[] counts;
private DoubleMatrix1D[] sums;
private DoubleMatrix2D[] inverseCovariances;
private double[] norms;
private Algebra alg;
private boolean readyToCall = false;
/**
* Constructor for BasecallingBaseModel
*/
public BasecallingBaseModel() {
counts = new double[4][4];
counts = new double[4];
runningChannelSums = new DoubleMatrix1D[4][4];
runningChannelProductSums = new DoubleMatrix2D[4][4];
sums = new DoubleMatrix1D[4];
inverseCovariances = new DoubleMatrix2D[4];
norms = new double[4];
means = new DoubleMatrix1D[4][4];
inverseCovariances = new DoubleMatrix2D[4][4];
norms = new double[4][4];
for (int basePrevIndex = 0; basePrevIndex < 4; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
runningChannelSums[basePrevIndex][baseCurIndex] = (DoubleFactory1D.dense).make(4);
runningChannelProductSums[basePrevIndex][baseCurIndex] = (DoubleFactory2D.dense).make(4, 4);
means[basePrevIndex][baseCurIndex] = (DoubleFactory1D.dense).make(4);
inverseCovariances[basePrevIndex][baseCurIndex] = (DoubleFactory2D.dense).make(4, 4);
}
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
sums[baseCurIndex] = (DoubleFactory1D.dense).make(4);
inverseCovariances[baseCurIndex] = (DoubleFactory2D.dense).make(4, 4);
}
alg = new Algebra();
@ -62,61 +51,63 @@ public class BasecallingBaseModel {
/**
* Add a single training point to the model.
*
* @param basePrev the previous cycle's base call (A, C, G, T, or * for the first cycle)
* @param baseCur the current cycle's base call (A, C, G, T)
* @param qualCur the quality score for the current cycle's base call
* @param fourintensity the four intensities for the current cycle's base call
*/
public void addTrainingPoint(char basePrev, char baseCur, byte qualCur, double[] fourintensity) {
int actualBasePrevIndex = baseToBaseIndex(basePrev);
public void addMeanPoint(char baseCur, byte qualCur, double[] fourintensity) {
int actualBaseCurIndex = baseToBaseIndex(baseCur);
double actualWeight = QualityUtils.qualToProb(qualCur);
double otherTheories = (basePrev == '*') ? 3.0 : 15.0;
cern.jet.math.Functions F = cern.jet.math.Functions.functions;
for (int basePrevIndex = 0; basePrevIndex < 4; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
// We want to upweight the correct theory as much as we can and spread the remainder out evenly between all other hypotheses.
double weight = (basePrevIndex == actualBasePrevIndex && baseCurIndex == actualBaseCurIndex) ? actualWeight : ((1.0 - actualWeight)/otherTheories);
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
// We want to upweight the correct theory as much as we can and spread the remainder out evenly between all other hypotheses.
double weight = (baseCurIndex == actualBaseCurIndex) ? actualWeight : ((1.0 - actualWeight)/3.0);
DoubleMatrix1D weightedChannelIntensities = (DoubleFactory1D.dense).make(fourintensity);
weightedChannelIntensities.assign(F.mult(weight));
DoubleMatrix1D weightedChannelIntensities = (DoubleFactory1D.dense).make(fourintensity);
weightedChannelIntensities.assign(F.mult(weight));
runningChannelSums[basePrevIndex][baseCurIndex].assign(weightedChannelIntensities, F.plus);
counts[basePrevIndex][baseCurIndex] += weight;
}
sums[baseCurIndex].assign(weightedChannelIntensities, F.plus);
counts[baseCurIndex] += weight;
}
/*
if (basePrevIndex >= 0 && baseCurIndex >= 0) {
for (int channel = 0; channel < 4; channel++) {
double weight = QualityUtils.qualToProb(qualCur);
double channelIntensity = fourintensity[channel];
runningChannelSums[basePrevIndex][baseCurIndex].setQuick(channel, runningChannelSums[basePrevIndex][baseCurIndex].getQuick(channel) + weight*channelIntensity);
for (int cochannel = 0; cochannel < 4; cochannel++) {
double cochannelIntensity = fourintensity[cochannel];
runningChannelProductSums[basePrevIndex][baseCurIndex].setQuick(channel, cochannel, runningChannelProductSums[basePrevIndex][baseCurIndex].getQuick(channel, cochannel) + weight*channelIntensity*cochannelIntensity);
}
}
counts[basePrevIndex][baseCurIndex]++;
}
*/
readyToCall = false;
}
public void addCovariancePoint(char baseCur, byte qualCur, double[] fourintensity) {
int actualBaseCurIndex = baseToBaseIndex(baseCur);
double actualWeight = QualityUtils.qualToProb(qualCur);
cern.jet.math.Functions F = cern.jet.math.Functions.functions;
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
// We want to upweight the correct theory as much as we can and spread the remainder out evenly between all other hypotheses.
double weight = (baseCurIndex == actualBaseCurIndex) ? actualWeight : ((1.0 - actualWeight)/3.0);
DoubleMatrix1D mean = sums[baseCurIndex].copy();
mean.assign(F.div(counts[baseCurIndex]));
DoubleMatrix1D sub = (DoubleFactory1D.dense).make(fourintensity);
sub.assign(mean, F.minus);
DoubleMatrix2D cov = (DoubleFactory2D.dense).make(4, 4);
alg.multOuter(sub, sub, cov);
cov.assign(F.mult(weight));
inverseCovariances[baseCurIndex].assign(cov, F.plus);
}
}
/**
* Precompute all the matrix inversions and determinants we'll need for computing the likelihood distributions.
*/
public void prepareToCallBases() {
/*
for (int basePrevIndex = 0; basePrevIndex < 4; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
for (int channel = 0; channel < 4; channel++) {
means[basePrevIndex][baseCurIndex].setQuick(channel, runningChannelSums[basePrevIndex][baseCurIndex].getQuick(channel)/counts[basePrevIndex][baseCurIndex]);
sums[baseCurIndex].setQuick(channel, runningChannelSums[basePrevIndex][baseCurIndex].getQuick(channel)/counts[basePrevIndex][baseCurIndex]);
for (int cochannel = 0; cochannel < 4; cochannel++) {
// Cov(Xi, Xj) = E(XiXj) - E(Xi)E(Xj)
@ -129,6 +120,7 @@ public class BasecallingBaseModel {
norms[basePrevIndex][baseCurIndex] = Math.pow(alg.det(invcov), 0.5)/Math.pow(2.0*Math.PI, 2.0);
}
}
*/
readyToCall = true;
}
@ -149,6 +141,7 @@ public class BasecallingBaseModel {
}
double[][] probdist = new double[4][4];
/*
double probPrev = (cycle == 0) ? 1.0 : QualityUtils.qualToProb(qualPrev);
int baseIndex = (cycle == 0) ? 0 : baseToBaseIndex(basePrev);
@ -156,7 +149,7 @@ public class BasecallingBaseModel {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
double[] diff = new double[4];
for (int channel = 0; channel < 4; channel++) {
diff[channel] = fourintensity[channel] - means[basePrevIndex][baseCurIndex].getQuick(channel);
diff[channel] = fourintensity[channel] - sums[basePrevIndex][baseCurIndex].getQuick(channel);
}
DoubleMatrix1D sub = (DoubleFactory1D.dense).make(diff);
@ -166,6 +159,7 @@ public class BasecallingBaseModel {
probdist[basePrevIndex][baseCurIndex] = (baseIndex == basePrevIndex ? probPrev : 1.0 - probPrev)*norms[basePrevIndex][baseCurIndex]*Math.exp(exparg);
}
}
*/
return probdist;
}
@ -174,15 +168,19 @@ public class BasecallingBaseModel {
try {
PrintWriter writer = new PrintWriter(outparam);
for (int basePrevIndex = 0; basePrevIndex < 4; basePrevIndex++) {
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
writer.print("mean_" + baseIndexToBase(basePrevIndex) + "" + baseIndexToBase(baseCurIndex) + " : [ ");
for (int channel = 0; channel < 4; channel++) {
writer.print(runningChannelSums[basePrevIndex][baseCurIndex].getQuick(channel)/counts[basePrevIndex][baseCurIndex]);
writer.print(" ");
}
writer.print("]\n");
for (int baseCurIndex = 0; baseCurIndex < 4; baseCurIndex++) {
writer.print("mean_" + baseIndexToBase(baseCurIndex) + " : [ ");
for (int channel = 0; channel < 4; channel++) {
writer.print(sums[baseCurIndex].getQuick(channel)/counts[baseCurIndex]);
writer.print(" ");
}
writer.print("] (" + counts[baseCurIndex] + ")\n");
DoubleMatrix2D cov = inverseCovariances[baseCurIndex].copy();
cern.jet.math.Functions F = cern.jet.math.Functions.functions;
cov.assign(F.div(counts[baseCurIndex]));
writer.println("cov_" + baseIndexToBase(baseCurIndex) + " : " + cov + "\n");
}
writer.close();
@ -193,6 +191,7 @@ public class BasecallingBaseModel {
/**
* Utility method for converting a base ([Aa*], [Cc], [Gg], [Tt]) to an index (0, 1, 2, 3);
*
* @param base
* @return 0, 1, 2, 3, or -1 if the base can't be understood.
*/

9
java/src/org/broadinstitute/sting/playground/fourbasecaller/BasecallingReadModel.java
View File

@ -32,13 +32,16 @@ public class BasecallingReadModel {
* Add a single training point to the model.
*
* @param cycle the cycle for which this point should be added
* @param basePrev the previous base
* @param baseCur the current base
* @param qualCur the current base's quality
* @param fourintensity the four intensities of the current base
*/
public void addTrainingPoint(int cycle, char basePrev, char baseCur, byte qualCur, double[] fourintensity) {
basemodels[cycle].addTrainingPoint(basePrev, baseCur, qualCur, fourintensity);
public void addMeanPoint(int cycle, char baseCur, byte qualCur, double[] fourintensity) {
basemodels[cycle].addMeanPoint(baseCur, qualCur, fourintensity);
}
public void addCovariancePoint(int cycle, char baseCur, byte qualCur, double[] fourintensity) {
basemodels[cycle].addCovariancePoint(baseCur, qualCur, fourintensity);
}
/**

37
java/src/org/broadinstitute/sting/playground/fourbasecaller/FourBaseRecaller.java
View File

@ -48,7 +48,9 @@ public class FourBaseRecaller extends CommandLineProgram {
BasecallingReadModel model = new BasecallingReadModel(bread.getFirstReadSequence().length());
int queryid;
// learn initial parameters
// learn mean parameters
//System.out.println("intensity int_a int_c int_g int_t base");
queryid = 0;
do {
String bases = (END <= 1) ? bread.getFirstReadSequence() : bread.getSecondReadSequence();
@ -56,22 +58,50 @@ public class FourBaseRecaller extends CommandLineProgram {
double[][] intensities = bread.getIntensities();
for (int cycle = 0; cycle < bases.length(); cycle++) {
char basePrev = (cycle == 0) ? '*' : bases.charAt(cycle - 1);
char baseCur = bases.charAt(cycle);
byte qualCur = quals[cycle];
double[] fourintensity = intensities[cycle + cycle_offset];
model.addTrainingPoint(cycle, basePrev, baseCur, qualCur, fourintensity);
/*
if (cycle == 0) {
System.out.println("intensity " + intensities[0][0] + " " + intensities[0][1] + " " + intensities[0][2] + " " + intensities[0][3] + " " + baseCur);
}
*/
model.addMeanPoint(cycle, baseCur, qualCur, fourintensity);
}
queryid++;
} while (queryid < TRAINING_LIMIT && bfp.hasNext() && (bread = bfp.next()) != null);
// learn covariance parameters
bfp = new BustardFileParser(DIR, LANE, isPaired, "FB");
bread = bfp.next();
queryid = 0;
do {
String bases = (END <= 1) ? bread.getFirstReadSequence() : bread.getSecondReadSequence();
byte[] quals = (END <= 1) ? bread.getFirstReadPhredBinaryQualities() : bread.getSecondReadPhredBinaryQualities();
double[][] intensities = bread.getIntensities();
for (int cycle = 0; cycle < bases.length(); cycle++) {
char baseCur = bases.charAt(cycle);
byte qualCur = quals[cycle];
double[] fourintensity = intensities[cycle + cycle_offset];
model.addCovariancePoint(cycle, baseCur, qualCur, fourintensity);
}
queryid++;
} while (queryid < TRAINING_LIMIT && bfp.hasNext() && (bread = bfp.next()) != null);
// write debugging info
File debugout = new File(OUT.getParentFile().getPath() + "/model/");
debugout.mkdir();
model.write(debugout);
// call bases
/*
SAMFileHeader sfh = new SAMFileHeader();
SAMFileWriter sfw = new SAMFileWriterFactory().makeSAMOrBAMWriter(sfh, false, OUT);
@ -107,6 +137,7 @@ public class FourBaseRecaller extends CommandLineProgram {
} while (queryid < CALLING_LIMIT && bfp.hasNext() && (bread = bfp.next()) != null);
sfw.close();
*/
return 0;
}