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/*
* The MIT License
*
* Copyright (c) 2017 The Broad Institute
*
* 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 picard.fingerprint;
import htsjdk.samtools.metrics.MetricsFile;
import htsjdk.samtools.util.IOUtil;
import org.broadinstitute.barclay.help.DocumentedFeature;
import picard.cmdline.CommandLineProgram;
import org.broadinstitute.barclay.argparser.CommandLineProgramProperties;
import org.broadinstitute.barclay.argparser.Argument;
import picard.cmdline.StandardOptionDefinitions;
import picard.cmdline.programgroups.DiagnosticsAndQCProgramGroup;
import picard.util.GraphUtils;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.stream.Collectors;
/**
*
Summary
* Clusters the results from a {@link CrosscheckFingerprints} run according to the LOD score. The resulting metric file
* can be used to assist diagnosing results from {@link CrosscheckFingerprints}. It clusters the connectivity graph between the
* different groups. Two groups are connected if they have a LOD score greater than the {@link #LOD_THRESHOLD}.
*
*
*
Details
* The results of running {@link CrosscheckFingerprints} can be difficult to analyze, especially when many groups are
* related (meaning LOD greater than {@link #LOD_THRESHOLD}) in non-transitive manner (A is related to B, B is related to C,
* but A doesn't seem to be related to C.) {@link ClusterCrosscheckMetrics} clusters the metrics from {@link CrosscheckFingerprints}
* so that all the groups in a cluster are related to each other either directly, or indirectly (thus A, B and C would
* end up in one cluster.) Two samples can only be in two different clusters if all the samples from these two clusters
* do not get high LOD scores when compared to each other.
*
*
*
* The resulting file, consists of the {@link ClusteredCrosscheckMetric} class and contains the original crosscheck metric
* values, for groups that end-up in the same clusters (regardless of LOD score of each comparison). In addition it notes
* the {@link ClusteredCrosscheckMetric#CLUSTER} identifier and the size of the cluster (in {@link ClusteredCrosscheckMetric#CLUSTER_SIZE}.)
* Groups that do not have high LOD scores with any other group (including itself!) will not be included in the metric file.
* Note that cross-group comparisons are not included in the metric file.
*
* @author Yossi Farjoun
*/
@CommandLineProgramProperties(
summary =
"
Summary
\n" +
"Clusters the results from a {@link CrosscheckFingerprints} run according to the LOD score. The resulting metric file " +
"can be used to assist diagnosing results from {@link CrosscheckFingerprints}. It clusters the connectivity graph between the " +
"different groups. Two groups are connected if they have a LOD score greater than the {@link #LOD_THRESHOLD}.\n " +
"\n" +
"
Details
" +
"The results of running {@link CrosscheckFingerprints} can be difficult to analyze, especially when many groups are " +
"related (meaning LOD greater than {@link #LOD_THRESHOLD}) in non-transitive manner (A is related to B, B is related to C, " +
"but A doesn't seem to be related to C.) {@link ClusterCrosscheckMetrics} clusters the metrics from {@link CrosscheckFingerprints} " +
"so that all the groups in a cluster are related to each other either directly, or indirectly (thus A, B and C would " +
"end up in one cluster.) Two samples can only be in two different clusters if all the samples from these two clusters " +
"do not get high LOD scores when compared to each other.\n" +
"\n" +
"
\n" +
"\n" +
"The resulting file, consists of the {@link ClusteredCrosscheckMetric} class and contains the original crosscheck metric " +
"values, for groups that end-up in the same clusters (regardless of LOD score of each comparison). In addition it notes " +
"the {@link ClusteredCrosscheckMetric#CLUSTER} identifier and the size of the cluster (in {@link ClusteredCrosscheckMetric#CLUSTER_SIZE}.) " +
"Groups that do not have high LOD scores with any other group (including " +
"itself!) will not be included in the metric file. Note that cross-group comparisons are not included in the metric file. ",
oneLineSummary = "Clusters the results of a CrosscheckFingerprints run by LOD score",
programGroup = DiagnosticsAndQCProgramGroup.class
)
@DocumentedFeature
public class ClusterCrosscheckMetrics extends CommandLineProgram {
@Argument(shortName = StandardOptionDefinitions.INPUT_SHORT_NAME,
doc = "The cross-check metrics file to be clustered.")
public File INPUT;
@Argument(shortName = StandardOptionDefinitions.OUTPUT_SHORT_NAME, optional = true,
doc = "Output file to write metrics to. Will write to stdout if null.")
public File OUTPUT;
@Argument(shortName = "LOD",
doc = "LOD score to be used as the threshold for clustering.")
public double LOD_THRESHOLD = 0;
@Override
protected int doWork() {
IOUtil.assertFileIsReadable(INPUT);
if(OUTPUT != null) IOUtil.assertFileIsWritable(OUTPUT);
final MetricsFile metricsFile = getMetricsFile();
try {
metricsFile.read(new FileReader(INPUT));
} catch (FileNotFoundException e) {
e.printStackTrace();
return 1;
}
clusterMetrics(metricsFile.getMetrics()).write(OUTPUT);
return 0;
}
private MetricsFile clusterMetrics(final List metrics) {
final GraphUtils.Graph graph = new GraphUtils.Graph<>();
metrics.stream()
.filter(metric -> metric.LOD_SCORE > LOD_THRESHOLD)
.forEach(metric -> {
final String lhsBy = metric.LEFT_GROUP_VALUE;
final String rhsBy = metric.RIGHT_GROUP_VALUE;
graph.addEdge(lhsBy, rhsBy);
});
final Map clusters = graph.cluster();
// invert map...get map from group integer to list of group_value
final Map> collection = clusters.entrySet().stream()
.collect(Collectors.groupingBy(Map.Entry::getValue))
.entrySet()
.stream()
.collect(Collectors
.toMap(Map.Entry::getKey, entry -> entry.getValue()
.stream()
.map(Map.Entry::getKey)
.collect(Collectors.toSet())));
final MetricsFile clusteredMetrics = getMetricsFile();
// for each cluster, find the metrics that compare groups that are both from the cluster
// and add them to the metrics file
for (final Map.Entry> cluster : collection.entrySet()) {
clusteredMetrics.addAllMetrics(
metrics.stream()
.filter(metric ->
cluster.getValue().contains(metric.LEFT_GROUP_VALUE) &&
cluster.getValue().contains(metric.RIGHT_GROUP_VALUE))
.map(metric -> {
final ClusteredCrosscheckMetric clusteredCrosscheckMetric = new ClusteredCrosscheckMetric(metric);
clusteredCrosscheckMetric.CLUSTER = cluster.getKey();
clusteredCrosscheckMetric.CLUSTER_SIZE = cluster.getValue().size();
return clusteredCrosscheckMetric;
})
.collect(Collectors.toSet()));
}
return clusteredMetrics;
}
}
