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This Agreement shall be construed, governed, interpreted and applied in accordance with the internal laws of the Commonwealth of Massachusetts, U.S.A., without regard to conflict of laws principles. */ package org.broadinstitute.gatk.tools.walkers.cancer.contamination; import org.apache.commons.math.MathException; import org.apache.commons.math.distribution.BetaDistribution; import org.apache.commons.math.distribution.BetaDistributionImpl; import org.broadinstitute.gatk.utils.GenomeLoc; import org.broadinstitute.gatk.utils.Utils; import java.io.*; import java.util.*; /** * our contamination results object; this object aggregates the results of the contamination run over lanes, samples, * or whatever other divisor we've used on the read data */ public class ContaminationResults { public static class ContaminationData implements Comparable<ContaminationData> { private GenomeLoc site; private long basesMatching = 0l; private long basesMismatching = 0l; private double mismatchFraction = -1d; private double[] bins; private double p; public long getBasesMatching() { return basesMatching; } public long getBasesMismatching() { return basesMismatching; } public double getMismatchFraction() { return mismatchFraction; } public double[] getBins() { return bins; } public double getP() { return p; } public ContaminationData(GenomeLoc site, long basesMatching, long basesMismatching, double[] bins) { this.site = site; this.basesMatching = basesMatching; this.basesMismatching = basesMismatching; this.bins = bins; long totalBases = this.basesMatching + this.basesMismatching; if (totalBases != 0) { this.mismatchFraction = (double) this.basesMismatching / (double) totalBases; } int a = (int) this.getBasesMismatching() + 1; int b = (int) this.getBasesMatching() + 1; BetaDistribution dist = new BetaDistributionImpl(a, b); try { this.p = 1.0d - dist.cumulativeProbability(0.5d); } catch (MathException me) { throw new RuntimeException("Error! - " + me.toString()); } } public int compareTo(ContaminationData other) { return -Double.compare(this.getP(), other.getP()); } @Override public String toString() { return "ContaminationData{" + "site=" + site + ", basesMatching=" + basesMatching + ", basesMismatching=" + basesMismatching + ", mismatchFraction=" + mismatchFraction + '}'; } } // what precision are we using in our calculations private final double precision; // a map of our contamination targets and their stats // key: aggregation entity ("META", sample name, or lane name) // value: ContaminationStats (whcih private Map<String, Map<String, ContaminationStats>> stats = new HashMap<String, Map<String, ContaminationStats>>(); public ContaminationResults(double precision) { this.precision = precision; } Map<String, Map<String, List<ContaminationData>>> storedData = new HashMap<String, Map<String, List<ContaminationData>>>(); /** * add to the stats * * @param newAggregationStats a mapping of the stat name to their statistics collected */ public void add(Map<String, Map<String, ContaminationStats>> newAggregationStats) { // for each aggregation level for (String aggregationKey : newAggregationStats.keySet()) { Map<String, ContaminationStats> populationContaminationStats = newAggregationStats.get(aggregationKey); // a new way of doing this... store all the data until the end... if (!storedData.containsKey(aggregationKey)) { storedData.put(aggregationKey, new HashMap<String, List<ContaminationData>>()); } for (String pop : populationContaminationStats.keySet()) { ContaminationStats newStats = populationContaminationStats.get(pop); // if it exists... just merge it if (!storedData.get(aggregationKey).containsKey(pop)) { storedData.get(aggregationKey).put(pop, new ArrayList<ContaminationData>()); } double[] newData = new double[newStats.getContamination().getBins().length]; System.arraycopy(newStats.getContamination().getBins(), 0, newData, 0, newStats.getContamination().getBins().length); storedData.get(aggregationKey).get(pop).add(new ContaminationData(newStats.getSite(), newStats.getBasesMatching(), newStats.getBasesMismatching(), newData)); } // merge the sets if (stats.containsKey(aggregationKey)) { // and for each population for (String pop : populationContaminationStats.keySet()) { ContaminationStats newStats = populationContaminationStats.get(pop); // if it exists... just merge it if (stats.get(aggregationKey).containsKey(pop)) { stats.get(aggregationKey).get(pop).add(newStats); } else { stats.get(aggregationKey).put(pop, newStats); } } } else { stats.put(aggregationKey, populationContaminationStats); } } } /** * output the contamination data, and return the contamination data * @param out the output source * @return the contamination value */ public void outputReport(double precision, PrintStream out, double fractionToTrim, double trimInterval, double betaThreshold) { out.println( "name\tpopulation\tpopulation_fit\tcontamination\tconfidence_interval_95_width\tconfidence_interval_95_low\tconfidence_interval_95_high\tsites"); for (Map.Entry<String, Map<String, ContaminationStats>> entry : stats.entrySet()) { for (ContaminationStats stats : entry.getValue().values()) { String aggregationLevel = entry.getKey(); String population = stats.getContamination().getPopultationName(); List<ContaminationData> newStats = storedData.get(aggregationLevel).get(population); String pm = "%3." + Math.round(Math.log10(1 / precision)) + "f"; int bins = newStats.iterator().next().getBins().length; int maxTrim = (int) Math.floor((double) (newStats.size()) * fractionToTrim); // sort the collection Collections.sort(newStats); List<ContaminationData> data = new ArrayList<ContaminationData>(newStats); // trim sites with > 95% p of being > 0.5 f (based on beta distribution) int trimmed = 0; for (Iterator<ContaminationData> i = data.iterator(); trimmed < maxTrim && i.hasNext();) { ContaminationData x = i.next(); if (x.getP() >= betaThreshold) { System.out.println("Trimming " + x.toString() + " with p(f>=0.5) >= " + betaThreshold + " with a value of " + x.getP()); i.remove(); trimmed++; } } double[][] matrix = new double[bins][data.size()]; for (int i = 0; i < bins; i++) { for (int j = 0; j < data.size(); j++) { matrix[i][j] = data.get(j).getBins()[i]; } } // now perform the sum double[] output = new double[bins]; for (int i = 0; i < bins; i++) { double[] binData = matrix[i]; // remove the top and bottom output[i] = 0; for (int x = 0; x < binData.length; x++) { output[i] += binData[x]; } } double[] newTrimmedStats = output; // get the confidence interval, at the set width ContaminationEstimate.ConfidenceInterval newInterval = new ContaminationEstimate.ConfidenceInterval( newTrimmedStats, 0.95); out.println(String.format("%s\t%s\t%s\t" + pm + "\t" + pm + "\t" + pm + "\t" + pm + "\t" + "%d", aggregationLevel, population, "n/a", newInterval.getContamination(), (newInterval.getStop() - newInterval.getStart()), newInterval.getStart(), newInterval.getStop(), data.size())); } } } public void writeCurves(PrintStream out) { boolean outputBins = false; for (Map.Entry<String, Map<String, ContaminationStats>> entry : stats.entrySet()) { for (ContaminationStats stats : entry.getValue().values()) { if (!outputBins) { String[] bins = new String[stats.getContamination().getBins().length]; for (int index = 0; index < stats.getContamination().getBins().length; index++) bins[index] = String .valueOf(100.0 * (1 - (double) index / stats.getContamination().getBins().length)); outputBins = true; out.print("name,pop,"); out.println(Utils.join(",", bins)); } String[] bins = new String[stats.getContamination().getBins().length]; int index = 0; for (double value : stats.getContamination().getBins()) bins[index++] = String.valueOf(value); out.print(entry.getKey() + ",\"" + stats.getContamination().getPopultationName() + "\","); out.println(Utils.join(",", bins)); } } } public Map<String, Map<String, ContaminationStats>> getStats() { return Collections.unmodifiableMap(stats); } public void setStats(Map<String, Map<String, ContaminationStats>> stats) { this.stats = stats; } }