One line format is useable and two levels of debug output are available (debug = 1: one line format, debug = 2: table of sampled probs for each locus). Class AlleleFrequencyMetrics computes %dbSNP and frequency of SNPs.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@99 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
andrewk
parent
f1034f3dfd
commit
5fa99f430e
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@ -3,26 +3,27 @@ package org.broadinstitute.sting.gatk.walkers;
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//import org.broadinstitute.sting.gatk.iterators.LocusIterator;
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import org.broadinstitute.sting.gatk.LocusContext;
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import org.broadinstitute.sting.gatk.refdata.ReferenceOrderedDatum;
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import org.broadinstitute.sting.gatk.refdata.rodDbSNP;
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import org.broadinstitute.sting.utils.AlleleFrequencyMetrics;
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import net.sf.samtools.SAMRecord;
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import java.util.List;
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import java.util.Arrays;
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public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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public class AlleleFrequencyWalker extends BasicLociWalker<AlleleFrequencyWalker.AlleleFrequencyEstimate, Integer> {
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public Integer map(List<ReferenceOrderedDatum> rodData, char ref, LocusContext context) {
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public AlleleFrequencyEstimate map(List<ReferenceOrderedDatum> rodData, char ref, LocusContext context) {
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// Convert context data into bases and 4-base quals
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// Set number of chromosomes, N, to 2 for now
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int N = 2;
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boolean debug = false;
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int debug = 0;
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// Convert bases to CharArray
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int numReads = context.getReads().size(); //numReads();
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byte[] bases = new byte[numReads];
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String base_string = "";
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double[][] quals = new double[numReads][4];
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int refnum = nuc2num[ref];
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@ -33,6 +34,7 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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int offset = offsets.get(i);
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bases[i] = read.getReadBases()[offset];
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base_string += read.getReadString().charAt(offset);
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int callednum = nuc2num[bases[i]];
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quals[i][callednum] = 1 - Math.pow(10.0, (double)read.getBaseQualities()[offset] / -10.0);
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@ -60,21 +62,33 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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}
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assert(altnum > 0);
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if (bases.length > 0) { //altcount > 2) {
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System.out.format("Pos: %s | ref: %c | alt: %c | %2dA | %2dC | %2dT | %2dG | %2d total | ", context.getLocation(), num2nuc[refnum],
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num2nuc[altnum], base_counts[0], base_counts[1], base_counts[2], base_counts[3], bases.length);
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if (debug >= 1) System.out.format("Pos: %s ref: %c alt: %c ", context.getLocation(), num2nuc[refnum], num2nuc[altnum]);
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//System.out.format("%2dA %2dC %2dT %2dG %2d total", base_counts[0], base_counts[1], base_counts[2], base_counts[3], bases.length);
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if (debug) print_base_qual_matrix(quals, numReads);
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if (debug >= 2) print_base_qual_matrix(quals, numReads);
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// Check if we really want to do this one
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AlleleFrequencyEstimator(N, bases, quals, refnum, altnum, debug);
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AlleleFrequencyEstimate alleleFreq = AlleleFrequencyEstimator(N, bases, quals, refnum, altnum, debug);
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// Print dbSNP data if its there
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if (debug >= 1) {
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for ( ReferenceOrderedDatum datum : rodData ) {
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if ( datum != null && datum instanceof rodDbSNP) {
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rodDbSNP dbsnp = (rodDbSNP)datum;
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//System.out.printf(" DBSNP %s on %s => %s%n", dbsnp.toSimpleString(), dbsnp.strand, Utils.join("/", dbsnp.getAllelesFWD()));
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System.out.printf("ROD: %s ",dbsnp.toMediumString());
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}
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}
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}
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if (debug) System.out.println();
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if (debug >= 1) System.out.format(" %s\n", base_string);
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if (debug >= 2) System.out.println();
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return 1;
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alleleMetrics.calculateMetrics(rodData, alleleFreq);
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return alleleFreq;
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}
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static void AlleleFrequencyEstimator(int N, byte[] bases, double[][] quals, int refnum, int altnum, boolean debug) {
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AlleleFrequencyEstimate AlleleFrequencyEstimator(int N, byte[] bases, double[][] quals, int refnum, int altnum, int debug) {
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// q = hypothetical %nonref
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// qstar = true %nonref
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@ -83,47 +97,138 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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// alt = alternate hypothesis base (most frequent nonref base)
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// ref = reference base
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// b = number of bases at locus
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double epsilon = 0; // 1e-2;
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double qstar;
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if (debug) {
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System.out.format("%4s | ", "q ");
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int qstar_N;
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if (debug >= 2) {
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System.out.format("%4s ", "q ");
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for (qstar = epsilon; qstar <= 1.0; qstar += (1.0 - 2*epsilon)/N)
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System.out.format("%5s%12s %12s %12s | ", "qstar", "p(D|q) ", "p(q|G) ", "posterior ");
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if (debug) System.out.println();
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System.out.format("%5s%10s %10s %10s ", "qstar", "p(D|q) ", "p(q|G) ", "posterior ");
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System.out.println();
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}
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double highest_posterior = -1.0;
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double highest_qstar = -1.0;
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double highest_q = -1.0;
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MixtureLikelihood[] bestMixtures = { new MixtureLikelihood(-1,-1,-1), new MixtureLikelihood(-1,-1,-1), new MixtureLikelihood(-1,-1,-1) };
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/*double[] highest_posterior = new double[] {-1.0, -1.0, -1.0};
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double[] highest_qstar = new double[] {-1.0, -1.0, -1.0};
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double[] highest_q = new double[] {-1.0, -1.0, -1.0};*/
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double qstep = 0.01;
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double qend = 1.0 + qstep / 10; // makes sure we get to 1.0 even with rounding error of qsteps accumulating
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for (double q=0.0; q <= qend; q += qstep) {
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if (debug) System.out.format("%.2f | ", q);
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for (qstar = epsilon; qstar <= 1.0; qstar += (1.0 - 2*epsilon)/N) {
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double pDq = P_D_q(bases, quals, q, refnum, altnum);
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double pqG = P_q_G(bases, N, q, qstar);
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double pG = P_G(N, qstar, altnum);
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double posterior = pDq * pqG * pG;
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if (debug) System.out.format("%.2f %.10f %.10f %.10f | ", qstar, pDq, pqG, posterior);
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if (posterior > highest_posterior) {
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highest_posterior = posterior;
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highest_qstar = qstar;
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highest_q = q;
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}
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}
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if (debug) System.out.println();
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}
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/*System.out.printf("Maximal likelihood posterior: %.6f\n", highest_posterior);
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System.out.printf("q that maximimizes posterior: %.6f\n", highest_q);
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System.out.printf("qstar used when calculating max q: %.6f\n", highest_qstar);*/
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System.out.printf("qhat %.2f | qstar %.2f | ", highest_q, highest_qstar);
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for (double q=0.0; q <= qend; q += qstep) { // hypothetic allele balance that we sample over
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if (debug >= 2) System.out.format("%.2f ", q);
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// Print out the called bases
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long numNonrefBases = Math.round(highest_q * N);
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long numRefBases = N - numNonrefBases;
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String genotype = repeat(num2nuc[refnum], numRefBases) + repeat(num2nuc[altnum], numNonrefBases);
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System.out.printf("gen: %s\n", genotype);
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long q_R = Math.round(q*bases.length);
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for (qstar = epsilon, qstar_N = 0; qstar <= 1.0; qstar += (1.0 - 2*epsilon)/N, qstar_N++) { // qstar - true allele balances
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// for N=2: these are 0.0 + epsilon, 0.5, 1.0 - epsilon corresponding to reference, het-SNP, homo-SNP
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//int qstar_N = Math.round((float)qstar*N);
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double pDq = P_D_q(bases, quals, q, refnum, altnum);
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double pqG = P_q_G(bases, N, q, qstar, q_R);
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double pG = P_G(N, qstar_N); //= P_G(N, qstar);
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double posterior = pDq * pqG * pG;
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if (debug >= 2) System.out.format("%.2f %10.7f %10.7f %10.7f ", qstar, Math.log10(pDq), Math.log10(pqG), Math.log10(posterior));
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if (posterior > bestMixtures[qstar_N].posterior)
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bestMixtures[qstar_N] = new MixtureLikelihood(posterior, qstar, q);
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}
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if (debug >= 2) System.out.println();
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}
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// Calculate log-odds difference - must happen before mixtures are sorted by posterior prob.
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double logOddsVarRef = logOddsNonrefRef(bestMixtures);
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if (debug >= 1) System.out.printf("LOD(Var,Ref): %6.2f ", logOddsVarRef);
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// Print all mixture likelihoods in order of qstar
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for (MixtureLikelihood mix : bestMixtures) // outputs: "het 3.26 "
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if (debug >= 1) System.out.printf("%3s: %6.2f ", genotypeTypeString(mix.qstar, N), Math.log10(mix.posterior / (1-mix.posterior)));
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// Sort by posterior probability
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Arrays.sort(bestMixtures); // We only used best entry, so this could be a generic max function (optimization)
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if (debug >= 1) System.out.printf("qhat: %.2f qstar: %.2f ", bestMixtures[0].qhat, bestMixtures[0].qstar); //highest_q, highest_qstar);
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if (debug >= 1) System.out.printf("prob: %.2e ", bestMixtures[0].posterior);
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if (debug >= 1) System.out.printf("type: %3s ", genotypeTypeString(bestMixtures[0].qstar, N));
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double posterior_null_hyp = P_D_q(bases, quals, 0.0, refnum, altnum) * P_q_G(bases, N, 0.0, 0.0, 0) * P_G(N, 0);
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if (debug >= 1) System.out.printf("P(null): %.2e ", posterior_null_hyp);
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//double LOD = Math.log10(bestMixtures[0].posterior) - Math.log10(posterior_null_hyp);
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double LOD = logOdds(bestMixtures[0].posterior, posterior_null_hyp);
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if (debug >= 1) System.out.printf("LOD: %6.2f ", LOD);
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AlleleFrequencyEstimate alleleFreq = new AlleleFrequencyEstimate(num2nuc[refnum], num2nuc[refnum],
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N, bestMixtures[0].qhat, bestMixtures[0].qstar, logOddsVarRef);
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if (debug >= 1) System.out.printf("gen: %s", genotypeTypeString(bestMixtures[0].qstar, N));
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return alleleFreq;
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}
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public class MixtureLikelihood implements Comparable<MixtureLikelihood> {
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public double posterior;
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public double qstar;
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public double qhat;
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MixtureLikelihood(double posterior, double qstar, double qhat) {
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this.posterior = posterior;
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this.qstar = qstar;
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this.qhat = qhat;
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}
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public int compareTo (MixtureLikelihood other) {
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// default sorting is REVERSE sorting on posterior prob; gives array with top post. prob. as first element
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if (posterior < other.posterior) return 1;
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else if (posterior > other.posterior) return -1;
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else return 0;
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}
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}
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public class AlleleFrequencyEstimate {
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//AlleleFrequencyEstimate();
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char ref;
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char alt;
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int N;
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double qhat;
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double qstar;
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double logOddsVarRef;
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public double getQstar() {return qstar;}
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public double getLogOddsVarRef() {return logOddsVarRef;}
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public AlleleFrequencyEstimate (char ref, char alt, int N, double qhat, double qstar, double logOddsVarRef) {
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this.ref = ref;
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this.alt = alt;
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this.N = N;
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this.qhat = qhat;
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this.qstar = qstar;
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this.logOddsVarRef = logOddsVarRef;
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}
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public String asString () {
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// Print out the called bases
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// Notes: switched from qhat to qstar because qhat doesn't work at n=1 (1 observed base) where having a single non-ref
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// base has you calculate qstar = 0.0 and qhat = 0.49 and that leads to a genotype predicition of AG according
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// to qhat, but AA according to qstar. This needs to be further investigated to see whether we really want
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// to use qstar, but make N (number of chormosomes) switch to n (number of reads at locus) for n=1
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long numNonrefBases = Math.round(qstar * N);
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long numRefBases = N - numNonrefBases;
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return repeat(ref, numRefBases) + repeat(alt, numNonrefBases);
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}
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}
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static double logOdds(double prob1, double prob2) {
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return Math.log10(prob1 / (1-prob1)) - Math.log10(prob2 / (1-prob2));
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}
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static double logOddsNonrefRef(MixtureLikelihood[] mixtures) {
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// takes list of mixtureLikelihoods SORTED BY QSTAR (default ordering)
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// Works for N == 2 right now!
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assert (mixtures.length == 2);
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MixtureLikelihood bestNonref = ((mixtures[1].posterior > mixtures[2].posterior) ? mixtures[1] : mixtures[2]);
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return logOdds(bestNonref.posterior, mixtures[0].posterior);
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// Code to calculate LOD using qstar=0 qhat=0
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//double posterior_null_hyp = P_D_q(bases, quals, 0.0, refnum, altnum) * P_q_G(bases, N, 0.0, 0.0) * P_G(N, 0.0);
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//double LOD = Math.log10(highest_posterior) - Math.log10(posterior_null_hyp);
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}
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static double P_D_q(byte[] bases, double[][]quals, double q, int refnum, int altnum) {
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@ -136,18 +241,30 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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return p;
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}
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static double P_q_G(byte [] bases, int N, double q, double qstar) {
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return binomialProb(Math.round(q*bases.length), bases.length, qstar);
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static double P_q_G(byte [] bases, int N, double q, double qstar, long q_R) {
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return binomialProb(q_R, bases.length, qstar);
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}
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static double P_G(int N, double qstar, int altnum) {
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static double P_G(int N, int qstar_N) {
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if (N==2) {
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return p_G_N_2[ Math.round((float)(qstar * N)) ];
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return p_G_N_2[ qstar_N ];
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}else{
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return 1.0;
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}
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}
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static String genotypeTypeString(double q, int N){
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switch (Math.round((float)q*N)) {
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case (0):
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return "ref";
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case (1):
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return "het";
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case (2):
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return "hom";
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}
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return "ERROR";
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}
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static double binomialProb(long k, long n, double p) {
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// k - numebr of successes
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// n - number of Bernoulli trials
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@ -168,6 +285,7 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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}
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public static String repeat(char letter, long count) {
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// Repeat a character count times
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String result = "";
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for (int i=0; i<count; i++) {
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result = result + letter;
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@ -178,8 +296,10 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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public Integer reduceInit() { return 0; }
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public Integer reduce(Integer value, Integer sum) {
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return value + sum;
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public Integer reduce(AlleleFrequencyEstimate alleleFreq, Integer sum) {
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//System.out.printf("%s %.2f\n", alleleFreq.asString(), alleleFreq.logOddsVarRef);
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return 0;//value + sum;
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}
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@ -207,8 +327,17 @@ public class AlleleFrequencyWalker extends BasicLociWalker<Integer, Integer> {
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p_G_N_2[0] = 0.999;
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p_G_N_2[1] = 1e-3;
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p_G_N_2[2] = 1e-5;
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alleleMetrics = new AlleleFrequencyMetrics();
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}
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AlleleFrequencyMetrics alleleMetrics;
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public void onTraversalDone() {
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alleleMetrics.printMetrics();
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}
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void print_base_qual_matrix(double [][]quals, int numReads) {
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// Print quals for debugging
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System.out.println("Base quality matrix");
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@ -0,0 +1,65 @@
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package org.broadinstitute.sting.utils;
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import org.broadinstitute.sting.gatk.refdata.ReferenceOrderedDatum;
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import org.broadinstitute.sting.gatk.refdata.rodDbSNP;
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import org.broadinstitute.sting.gatk.walkers.AlleleFrequencyWalker;
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import java.util.List;
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/**
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* Created by IntelliJ IDEA.
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* User: andrewk
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* Date: Mar 18, 2009
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* Time: 5:28:58 PM
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* To change this template use File | Settings | File Templates.
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*/
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public class AlleleFrequencyMetrics {
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long dbsnp_tp=0;
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long dbsnp_fp=0;
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long num_snps=0;
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long num_loci=0;
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public void calculateMetrics(List<ReferenceOrderedDatum> rodData, AlleleFrequencyWalker.AlleleFrequencyEstimate alleleFreq) {
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boolean is_dbSNP_SNP = false;
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for ( ReferenceOrderedDatum datum : rodData ) {
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if ( datum != null && datum instanceof rodDbSNP) {
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rodDbSNP dbsnp = (rodDbSNP)datum;
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if (dbsnp.isSNP()) is_dbSNP_SNP = true;
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}
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}
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if (alleleFreq.getQstar() > 0.0 && alleleFreq.getLogOddsVarRef() >= 5.0) { // we confidently called it a SNP!
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if (is_dbSNP_SNP) {
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dbsnp_tp += 1;
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}else{
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dbsnp_fp += 1;
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}
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}
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if (alleleFreq.getQstar() > 0.0) num_snps++;
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num_loci++;
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}
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public void printMetrics() {
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System.out.println("\nAllele Frequency Metrics:\n");
|
||||
System.out.printf("Precision of LOD >= 5 SNPs w.r.t dbSNP: %.2f\n", (float)dbsnp_tp / (dbsnp_fp + dbsnp_tp) * 100);
|
||||
System.out.printf("\\--TP: %d\n", dbsnp_tp);
|
||||
System.out.printf("\\--FP: %d\n", dbsnp_fp);
|
||||
System.out.println();
|
||||
System.out.printf("SNPs (LOD > 0): %d\n", num_snps);
|
||||
System.out.printf("Total loci: %d\n", num_loci);
|
||||
System.out.printf("SNPs / loci: 1/%.0f\n", (float)num_loci/num_snps);
|
||||
System.out.println();
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue