Examples with GenesetSimilarity org.baderlab.csplugins.enrichmentmap.model.GenesetSimilarity used on opensource projects

Example 6 with GenesetSimilarity

use of org.baderlab.csplugins.enrichmentmap.model.GenesetSimilarity in project EnrichmentMapApp by BaderLab.

the class CreateDiseaseSignatureNetworkTask method createEdge.

/**
	 * Returns true iff the user should be warned about an existing edge that
	 * does not pass the new cutoff. If the edge already exists it will be
	 * returned, if the edge had to be created it will not be returned.
	 */
private void createEdge(SimilarityKey similarityKey, CyNetwork network, CyNetworkView netView, String prefix, CyTable edgeTable, CyTable nodeTable, boolean passedCutoff, EMSignatureDataSet sigDataSet) {
    // PA always generates distinct edges
    final String edgeName = similarityKey.toString();
    GenesetSimilarity genesetSimilarity = geneSetSimilarities.get(similarityKey);
    CyEdge edge = existingEdgeCache.get(new EdgeCacheKey(edgeName, sigDataSet.getName()));
    if (edge == null) {
        if (passedCutoff) {
            CyNode hubNode = nodeCache.get(genesetSimilarity.getGeneset1Name());
            CyNode geneSet = NetworkUtil.getNodeWithValue(network, nodeTable, CyNetwork.NAME, genesetSimilarity.getGeneset2Name());
            if (hubNode == null || geneSet == null)
                return;
            edge = network.addEdge(hubNode, geneSet, false);
            // We actually add the edges to the EM data sets, not the signature data set
            sigDataSet.addEdgeSuid(edge.getSUID());
            //				map.getDataSet(similarityKey.getName()).addEdgeSuid(edge.getSUID());
            taskResult.addNewEdge(edge);
        } else {
            // edge does not exist and does not pass cutoff, do nothing
            return;
        }
    } else {
        if (!passedCutoff && existingEdgeCache.containsKey(edgeName))
            taskResult.addExistingEdgeFailsCutoff(edge);
    }
    if (passedCutoff)
        taskResult.incrementPassedCutoffCount();
    CyRow row = edgeTable.getRow(edge.getSUID());
    row.set(CyNetwork.NAME, edgeName);
    row.set(CyEdge.INTERACTION, similarityKey.getInteraction());
    List<String> geneList = new ArrayList<>();
    Set<Integer> genesHash = genesetSimilarity.getOverlappingGenes();
    for (Integer current : genesHash) {
        String gene = map.getGeneFromHashKey(current);
        if (gene != null)
            geneList.add(gene);
    }
    Collections.sort(geneList);
    Columns.EDGE_OVERLAP_GENES.set(row, prefix, null, geneList);
    Columns.EDGE_OVERLAP_SIZE.set(row, prefix, null, genesetSimilarity.getSizeOfOverlap());
    Columns.EDGE_SIMILARITY_COEFF.set(row, prefix, null, genesetSimilarity.getSimilarityCoeffecient());
    Columns.EDGE_DATASET.set(row, prefix, null, similarityKey.getName());
    Columns.EDGE_SIG_DATASET.set(row, prefix, null, sigDataSet.getName());
    if (passedCutoff)
        Columns.EDGE_CUTOFF_TYPE.set(row, prefix, null, params.getRankTestParameters().getType().display);
    PostAnalysisFilterType filterType = params.getRankTestParameters().getType();
    if (filterType.isMannWhitney()) {
        Columns.EDGE_MANN_WHIT_TWOSIDED_PVALUE.set(row, prefix, null, genesetSimilarity.getMannWhitPValueTwoSided());
        Columns.EDGE_MANN_WHIT_GREATER_PVALUE.set(row, prefix, null, genesetSimilarity.getMannWhitPValueGreater());
        Columns.EDGE_MANN_WHIT_LESS_PVALUE.set(row, prefix, null, genesetSimilarity.getMannWhitPValueLess());
        Columns.EDGE_MANN_WHIT_CUTOFF.set(row, prefix, null, params.getRankTestParameters().getValue());
    }
    // always calculate hypergeometric
    Columns.EDGE_HYPERGEOM_PVALUE.set(row, prefix, null, genesetSimilarity.getHypergeomPValue());
    Columns.EDGE_HYPERGEOM_U.set(row, prefix, null, genesetSimilarity.getHypergeomU());
    Columns.EDGE_HYPERGEOM_N.set(row, prefix, null, genesetSimilarity.getHypergeomN());
    Columns.EDGE_HYPERGEOM_M.set(row, prefix, null, genesetSimilarity.getHypergeomM());
    Columns.EDGE_HYPERGEOM_K.set(row, prefix, null, genesetSimilarity.getHypergeomK());
    Columns.EDGE_HYPERGEOM_CUTOFF.set(row, prefix, null, params.getRankTestParameters().getValue());
}

Example 7 with GenesetSimilarity

use of org.baderlab.csplugins.enrichmentmap.model.GenesetSimilarity in project EnrichmentMapApp by BaderLab.

the class ComputeSimilarityTaskParallel method computeGenesetSimilarity.

static GenesetSimilarity computeGenesetSimilarity(EMCreationParameters params, String geneset1Name, String geneset2Name, Set<Integer> geneset1, Set<Integer> geneset2, String dataset) {
    Set<Integer> intersection = Sets.intersection(geneset1, geneset2);
    Set<Integer> union = Sets.union(geneset1, geneset2);
    double coeffecient = computeSimilarityCoeffecient(params, intersection, union, geneset1, geneset2);
    if (coeffecient < params.getSimilarityCutoff())
        return null;
    String edgeType = params.getEnrichmentEdgeType();
    GenesetSimilarity similarity = new GenesetSimilarity(geneset1Name, geneset2Name, coeffecient, edgeType, intersection, dataset);
    return similarity;
}

Example 8 with GenesetSimilarity

use of org.baderlab.csplugins.enrichmentmap.model.GenesetSimilarity in project EnrichmentMapApp by BaderLab.

the class ComputeSimilarityTaskParallel method startComputeSimilarities.

private Map<SimilarityKey, GenesetSimilarity> startComputeSimilarities(TaskMonitor tm, ExecutorService executor, boolean distinct, boolean compound) {
    Set<String> names = map.getAllGeneSetOfInterestNames();
    Map<String, Set<Integer>> unionedGenesets = compound ? map.unionAllGeneSetsOfInterest() : null;
    DiscreteTaskMonitor taskMonitor = discreteTaskMonitor(tm, names.size());
    String edgeType = map.getParams().getEnrichmentEdgeType();
    Map<SimilarityKey, GenesetSimilarity> similarities = new ConcurrentHashMap<>();
    Collection<EMDataSet> dataSets = map.getDataSetList();
    for (final String geneset1Name : names) {
        // Compute similarities in batches, creating a Runnable for every similarity pair would create too many objects
        executor.execute(() -> {
            loop: for (final String geneset2Name : names) {
                if (Thread.interrupted())
                    break loop;
                if (geneset1Name.equalsIgnoreCase(geneset2Name))
                    //don't compare two identical gene sets
                    continue;
                if (distinct) {
                    for (EMDataSet dataset : dataSets) {
                        SimilarityKey key = new SimilarityKey(geneset1Name, geneset2Name, edgeType, dataset.getName());
                        if (!similarities.containsKey(key)) {
                            Map<String, GeneSet> genesets = dataset.getGeneSetsOfInterest().getGeneSets();
                            GeneSet geneset1 = genesets.get(geneset1Name);
                            GeneSet geneset2 = genesets.get(geneset2Name);
                            if (geneset1 != null && geneset2 != null) {
                                // returns null if the similarity coefficient doesn't pass the cutoff
                                GenesetSimilarity similarity = computeGenesetSimilarity(map.getParams(), geneset1Name, geneset2Name, geneset1.getGenes(), geneset2.getGenes(), dataset.getName());
                                if (similarity != null) {
                                    similarities.put(key, similarity);
                                }
                            }
                        }
                    }
                }
                if (compound) {
                    SimilarityKey key = new SimilarityKey(geneset1Name, geneset2Name, edgeType, null);
                    if (!similarities.containsKey(key)) {
                        Set<Integer> geneset1 = unionedGenesets.get(geneset1Name);
                        Set<Integer> geneset2 = unionedGenesets.get(geneset2Name);
                        // returns null if the similarity coefficient doesn't pass the cutoff
                        GenesetSimilarity similarity = computeGenesetSimilarity(map.getParams(), geneset1Name, geneset2Name, geneset1, geneset2, "compound");
                        if (similarity != null) {
                            similarities.put(key, similarity);
                        }
                    }
                }
            }
            taskMonitor.inc();
        });
    }
    return similarities;
}