private List<ClusteredResult> filterClusteredResult(List<ClusteredResult> crList) {
List<ClusteredResult> filteredList = new ArrayList<ClusteredResult>();
for (ClusteredResult cr : crList)
if (!cr.updatedResult.isEmpty())
if (cr.updatedResult.get(0).confidence >= minconf
&& cr.score >= minClusterScore
&& (minClusterFragRatio == -1 || cr.getFragRatio() >= minClusterFragRatio)
&& (minClusterMapSigRatio == -1 || cr.getMapSigRatio() >= minClusterMapSigRatio))
filteredList.add(cr);
return filteredList;
}
private List<ClusteredResult> standardnocluster(List<OptMapResultNode> mapList) {
List<ClusteredResult> clusteredResultList = new ArrayList<ClusteredResult>();
// VirtualMapProcessor vmProcessor1 = new VirtualMapProcessor(optrefmap, indelPenalty,
// inversionPenalty, translocationPenalty, indelFilter, inversionFilter, match, fpp, fnp,
// localPenalty);
for (OptMapResultNode result : mapList) {
List<OptMapResultNode> rmapList = new ArrayList<OptMapResultNode>();
rmapList.add(result);
ClusteredResult cr = new ClusteredResult();
// cr.importOriginalResult(rmapList);
cr.importUpdatedResult(rmapList);
cr.score = result.mappedscore;
// cr.process();
// if (cr.score < mins)
// mins = cr.score;
clusteredResultList.add(cr);
}
return clusteredResultList;
}
/**
* Cluster the results by using 1. Path builder filter which limits indel, inversion and
* translocation 2. vmProcessor to calculate the score
*
* <p>The logic includes two steps 1. Grouping 2. Build paths between partial maps (which may
* undergo trimming) 3. Link paths to paths (not partial maps to partial maps) 4. Get the path
* with best score and convert it back to partial maps
*
* @param mapList
* @param pbFilter
* @param vmProcessor
* @return
*/
private List<ClusteredResult> cluster(
List<OptMapResultNode> mapList, PathBuilderFilter pbFilter, VirtualMapProcessor vmProcessor) {
// exit if no result is input
if (mapList == null || mapList.size() == 0) return new ArrayList<ClusteredResult>();
// Pre-update the results
for (OptMapResultNode result : mapList) vmProcessor.calcScore(result);
List<ClusteredResult> clusteredResultList = new ArrayList<ClusteredResult>();
// Grouping close results to be 1. clustered effectively 2. output one clustered result per
// group
List<List<OptMapResultNode>> groupedMapList =
group(mapList, pbFilter.sameStrand, pbFilter.closeReference, pbFilter.closeFragment);
// In each result group
for (List<OptMapResultNode> groupedMap : groupedMapList) {
// Build a path whenever the results can be joined
// Please see ClusterPathNode for details
List<ClusterPathNode> clusterPathList = buildPath(groupedMap, pbFilter, vmProcessor);
// Link the paths
linkPath(clusterPathList, vmProcessor);
// Get the best path and extract the final result maps
List<OptMapResultNode> clusteredGroupMap =
convertPathToMapList(getBestPath(clusterPathList), vmProcessor);
// if (false)
// {
// for (ClusterPathNode cp : clusterPathList) {
// if (cp.currentScore < 0)
// continue;
// if (cp.secondMap != null)
// continue;
// List<ClusterPathNode> bestPath = new ArrayList<ClusterPathNode>();
// ClusterPathNode recentPath = cp;
// while (recentPath != null)
// {
// bestPath.add(recentPath);
// recentPath = recentPath.previousPath;
// }
// Collections.reverse(bestPath);
// if (convertPathToMapList(bestPath).isEmpty())
// continue;
// System.out.println("PATH - " + cp.currentScore);
// for (OptMapResultNode result : convertPathToMapList(bestPath))
// System.out.println(result);
// }
// }
ClusteredResult cr = new ClusteredResult();
// We want to retain the original alignments
List<OptMapResultNode> expandedGroupedMap =
new ArrayList<OptMapResultNode>(); // Saving the original results
for (OptMapResultNode map : groupedMap) expandedGroupedMap.addAll(map.getRealMap());
// We want to import the modified alignments
List<OptMapResultNode> expandedClusterGroupMap =
new ArrayList<OptMapResultNode>(); // Saving the updated results
for (OptMapResultNode map : clusteredGroupMap)
expandedClusterGroupMap.addAll(map.getRealMap());
// checkDirectLink(expandedClusterGroupMap, vmProcessor);
cr.importUpdatedResult(expandedClusterGroupMap);
if (cr.updatedResult.size() > 0) {
cr.process(vmProcessor);
clusteredResultList.add(cr);
}
}
return clusteredResultList;
}
