diff --git a/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/PathwayTopologyAnalysis.java b/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/PathwayTopologyAnalysis.java
new file mode 100644
index 0000000000000000000000000000000000000000..8f910ae6f787116016a9fcda48c03e0aef7ba2d0
--- /dev/null
+++ b/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/PathwayTopologyAnalysis.java
@@ -0,0 +1,211 @@
+package fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze;
+
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioMetabolite;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioNetwork;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioPathway;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.centrality.EigenVectorCentrality;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.centrality.PathBasedCentrality;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.CompoundGraph;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.ReactionEdge;
+
+import java.util.*;
+import java.util.function.BiFunction;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+
+/**
+ * Class to aggregate metabolites' centrality into pathway score for Pathway Topology Analysis
+ */
+public class PathwayTopologyAnalysis {
+
+ private Map<BioPathway,Collection<BioMetabolite>> kb;
+ private CompoundGraph g;
+ private Set<BioMetabolite> data;
+
+ private boolean normalize;
+
+ /**
+ * Instantiate class to perform Pathway Topology Analysis
+ * @param knowledgeBase Map affiliating metabolites to pathways
+ * @param topology CompoundGraph storing metabolites' relationships
+ * @param compoundOfInterest Set containing input data (significantly overrepresented metabolites for example)
+ */
+ public PathwayTopologyAnalysis(Map<BioPathway,Collection<BioMetabolite>> knowledgeBase, CompoundGraph topology, Set<BioMetabolite> compoundOfInterest){
+ this.kb=knowledgeBase;
+ this.g = topology;
+ this.data=compoundOfInterest;
+ }
+
+ /**
+ * Instantiate class to perform Pathway Topology Analysis
+ * @param knowledgeBase BioNetwork affiliating metabolites to pathways
+ * @param topology CompoundGraph storing metabolites' relationships
+ * @param compoundOfInterest Set containing input data (significantly overrepresented metabolites for example)
+ */
+ public PathwayTopologyAnalysis(BioNetwork knowledgeBase, CompoundGraph topology, Set<BioMetabolite> compoundOfInterest){
+ this.kb=new HashMap<>();
+ for(BioPathway p : knowledgeBase.getPathwaysView()){
+ kb.put(p,knowledgeBase.getMetabolitesFromPathway(p));
+ }
+ this.g = topology;
+ this.data=compoundOfInterest;
+ }
+
+ /**
+ * Use normalized score, using the ratio between the raw pathway score and the maximum score by pathway
+ * @return a PathwayTopologyAnalysis object
+ */
+ public PathwayTopologyAnalysis useNormalization(){
+ this.normalize=true;
+ return this;
+ }
+
+ /**
+ * Method to compute Pathway Impact from data, according to a given strategy for individual scoring and aggregation
+ * @param scoring an IndividualScoringStrategy for scoring metabolites (centrality measure)
+ * @param aggregation an AggregationStrategy for aggregating component scores into pathway score.
+ * @return a Map of Pathways and their score
+ */
+ public Map<BioPathway,Double> run(IndividualScoringStrategy scoring, AggregationStrategy aggregation){
+ Map<BioPathway,Double> pathwayFinalScore = computePathwayScore(data, g, scoring, aggregation);
+ if(normalize){
+ //create background data (i.e dataset with all compounds in network)
+ Set<BioMetabolite> background = kb.values().stream().flatMap(Collection::stream).filter(v -> g.vertexSet().contains(v))
+ .collect(Collectors.toSet());
+
+ //compute pathway score as if all their compounds were in dataset (recompute individual score for all compounds)
+ Map<BioPathway,Double> byPathwayBackgroundScore = computePathwayScore(background, g, scoring, aggregation);
+ //set final pathway score as ratio between pathway score computed from data and theoretical maximal pathway score
+ pathwayFinalScore = Stream.concat(pathwayFinalScore.entrySet().stream(), byPathwayBackgroundScore.entrySet().stream())
+ .collect(Collectors.toMap(
+ Map.Entry::getKey,
+ Map.Entry::getValue,
+ (value1, value2) -> value1/value2));
+ }
+ return pathwayFinalScore;
+ }
+
+ /*
+ Compute topology pathway analysis
+ */
+ private Map<BioPathway,Double> computePathwayScore(Set<BioMetabolite> data, CompoundGraph g, IndividualScoringStrategy scoring, AggregationStrategy aggregation){
+ //filter kb to keep only mapped pathways
+ kb = kb.entrySet().stream().filter(e -> e.getValue().stream().anyMatch(data::contains)).collect(Collectors.toMap(e->e.getKey(),e->e.getValue()));
+ //From compounds and given interaction network, compute topology score for each compound, using scoring strategy.
+ Map<BioMetabolite,Double> individualScore = scoring.apply(data,g);
+ //From knowledge base, get the pathway memberships and collect component's scores.
+ Map<BioPathway,Collection<Double>> pathwayScores = individualScoresByPathway(individualScore);
+ //Using aggregation strategy, compute for each pathway its final score from its constituents ones.
+ Map<BioPathway,Double> pathwayFinalScore = aggregation.apply(pathwayScores);
+ return pathwayFinalScore;
+ }
+
+ /*
+ From associated compound given by knowledge base, retrieve list of components' scores for each pathway
+ */
+ private Map<BioPathway,Collection<Double>> individualScoresByPathway(Map<BioMetabolite,Double> individualScore){
+ HashMap<BioPathway,Collection<Double>> pathwayScores = new HashMap<>();
+ for(Map.Entry<BioPathway,Collection<BioMetabolite>> pathwayEntry : kb.entrySet()){
+ List<Double> componentsScore = pathwayEntry.getValue().stream()
+ .filter(individualScore::containsKey)
+ .map(individualScore::get)
+ .collect(Collectors.toList());
+ pathwayScores.put(pathwayEntry.getKey(),componentsScore);
+ }
+ return pathwayScores;
+ }
+
+ /**
+ * Interface for individual scoring strategy, computing metabolites impact
+ */
+ public interface IndividualScoringStrategy extends BiFunction<Set<BioMetabolite>,CompoundGraph,Map<BioMetabolite,Double>> {
+
+ /**
+ * Use betweenness as scoring function, i.e. the proportion of shortest paths passing through a given node (excluding paths where it is the starting or ending node).
+ * @return a map of compounds and their respective impact score.
+ */
+ static IndividualScoringStrategy betweenness(){
+ return (Set<BioMetabolite> data,CompoundGraph graph) -> {
+ PathBasedCentrality<BioMetabolite, ReactionEdge,CompoundGraph> centralityAnalyser = new PathBasedCentrality<>(graph);
+ Map<BioMetabolite, Integer> betweenness = centralityAnalyser.getGeodesicBetweenness();
+ return betweenness.entrySet().stream()
+ .filter(e -> data.contains(e.getKey()))
+ .collect(Collectors.toMap(e -> e.getKey(), e -> Double.valueOf(betweenness.get(e.getKey()))));
+ };
+ }
+
+ /**
+ * Use PageRank as scoring function, a centrality measure that represents the likelihood that a random walk reach a particular node.
+ * This is run with default damping factor 0.85, using power iteration approximation with 15000 max iterations and 0.001 tolerance for convergence
+ * @return a map of compounds and their respective impact score.
+ */
+ static IndividualScoringStrategy pageRank(){
+ return IndividualScoringStrategy.pageRank(0.85,15000,0.001);
+ }
+
+ /**
+ * Use PageRank as scoring function, a centrality measure that represents the likelihood that a random walk reach a particular node
+ * @param dampingFactor damping factor
+ * @param maxIter maximal number of iteration of the power method
+ * @param tolerance convergence tolerance
+ * @return a map of compounds and their respective impact score.
+ */
+ static IndividualScoringStrategy pageRank(Double dampingFactor, int maxIter, double tolerance){
+ return (Set<BioMetabolite> data,CompoundGraph graph) -> {
+ EigenVectorCentrality<BioMetabolite, ReactionEdge,CompoundGraph> centralityAnalyser = new EigenVectorCentrality<>(graph);
+ centralityAnalyser.addJumpProb(graph.vertexSet().stream()
+ .map(BioMetabolite::getId).collect(Collectors.toSet()), dampingFactor);
+ Map<String, Double> pageRank = centralityAnalyser.computePowerMethodPageRank(dampingFactor,maxIter,tolerance);
+ return pageRank.entrySet().stream()
+ .filter(e -> data.stream().map(BioMetabolite::getId).collect(Collectors.toSet()).contains(e.getKey()))
+ .collect(Collectors.toMap(e -> graph.getVertex(e.getKey()), e -> e.getValue()));
+ };
+ }
+
+ /**
+ * Use out degree as scoring function, i.e. the number of outgoing edges of a node.
+ * @return a map of compounds and their respective impact score.
+ */
+ static IndividualScoringStrategy outDegree(){
+ return (Set<BioMetabolite> data,CompoundGraph graph) -> data.stream()
+ .collect(Collectors.toMap(v -> v, v -> Double.valueOf(graph.outDegreeOf(v))));
+ }
+
+ /**
+ * Use number of neighbors as scoring function. Contrary to degree, this is not impacted by parallel edges
+ * (same pairs of nodes connected by different edges corresponding to different reactions)
+ * @return a map of compounds and their respective impact score.
+ */
+ static IndividualScoringStrategy neighbors(){
+ return (Set<BioMetabolite> data,CompoundGraph graph) -> data.stream()
+ .collect(Collectors.toMap(v -> v, v -> Double.valueOf(graph.neighborListOf(v).size())));
+ }
+
+ }
+
+ /**
+ * Interface for aggregation strategy, computing pathway impact from constituting compounds' impact
+ */
+ public interface AggregationStrategy extends Function<Map<BioPathway,Collection<Double>>,Map<BioPathway,Double>> {
+
+ /**
+ * Simply count the sum of compounds of interest scores as the final pathway score
+ * @return
+ */
+ static AggregationStrategy rawSum(){
+ return (Map<BioPathway,Collection<Double>> pathwayScores) ->
+ {
+ Map<BioPathway,Double> pathwayFinalScore = new HashMap<>();
+ for(Map.Entry<BioPathway,Collection<Double>> e : pathwayScores.entrySet()){
+ Double finalScoring = 0.0;
+ for(Double score : e.getValue()){
+ finalScoring+=score;
+ }
+ pathwayFinalScore.put(e.getKey(),finalScoring);
+ }
+ return pathwayFinalScore;
+ };
+ }
+ }
+}
diff --git a/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/centrality/EigenVectorCentrality.java b/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/centrality/EigenVectorCentrality.java
index d3f0a32c5d91d9f57cb83c2589331f736d072358..d1893e22c3ac2126cbf7ccf91ae1d2ca3ba5df32 100644
--- a/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/centrality/EigenVectorCentrality.java
+++ b/met4j-graph/src/main/java/fr/inrae/toulouse/metexplore/met4j_graph/computation/analyze/centrality/EigenVectorCentrality.java
@@ -35,19 +35,15 @@
*/
package fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.centrality;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioEntity;
import fr.inrae.toulouse.metexplore.met4j_graph.computation.utils.ComputeAdjacencyMatrix;
import fr.inrae.toulouse.metexplore.met4j_graph.core.BioGraph;
import fr.inrae.toulouse.metexplore.met4j_graph.core.Edge;
import fr.inrae.toulouse.metexplore.met4j_mathUtils.matrix.BioMatrix;
import fr.inrae.toulouse.metexplore.met4j_mathUtils.matrix.EjmlMatrix;
-import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioEntity;
+
+import java.util.*;
+import java.util.stream.Collectors;
/**
* Class to compute the eigen vector centrality of each vertex in a BioGraph.
@@ -134,17 +130,27 @@ public class EigenVectorCentrality<V extends BioEntity, E extends Edge<V>, G ext
/**
* Gets a map with all nodes id as key and global page rank
*
- * @param d the damping factor
+ * @param dampingFactor the damping factor
* @return the map with node identifier and corresponding centrality
*/
- public HashMap<String, Double> computePageRank(double d){
+ public HashMap<String, Double> computePageRank(double dampingFactor){
BioMatrix tmp = adjacencyMatrix.copy();
- addJumpProb(adjacencyMatrix.getRowLabelMap().keySet(),d);
+ addJumpProb(adjacencyMatrix.getRowLabelMap().keySet(),1-dampingFactor);
HashMap<String, Double> result = computeEigenVectorCentrality();
adjacencyMatrix = tmp;
return result;
}
+ public HashMap<String, Double> computePowerMethodPageRank(double dampingFactor, int maxNbOfIter, double tolerance){
+ BioMatrix tmp = adjacencyMatrix.copy();
+ Set<String> allNodes = adjacencyMatrix.getRowLabelMap().keySet();
+ addJumpProb(allNodes,1 - dampingFactor);
+ Map<String, Double> seeds = allNodes.stream().collect(Collectors.toMap(k -> k, k -> 1.0 / allNodes.size()));
+
+ HashMap<String, Double> result = powerIteration(seeds, maxNbOfIter, tolerance);
+ adjacencyMatrix = tmp;
+ return result;
+ }
/**
* add a constant probability to "jump" (i.e. go to another node without necessarily following an edge) to defined set of node
@@ -329,7 +335,7 @@ public class EigenVectorCentrality<V extends BioEntity, E extends Edge<V>, G ext
* @param tol the tolerance, if the max delta between two iteration is below this value, the result is returned
* @return map with node id as key and eigen vector centrality as value
*/
- public HashMap<String, Double> powerIteration(HashMap<String, Double> seeds, int maxIter, double tol){
+ public HashMap<String, Double> powerIteration(Map<String, Double> seeds, int maxIter, double tol){
BioMatrix rank = new EjmlMatrix(1, adjacencyMatrix.numCols());
for(Map.Entry<String,Integer> entry : adjacencyMatrix.getRowLabelMap().entrySet()){
String e = entry.getKey();
diff --git a/met4j-graph/src/test/java/fr/inrae/toulouse/metexplore/met4j_graph/TestPathwayTopologyAnalysis.java b/met4j-graph/src/test/java/fr/inrae/toulouse/metexplore/met4j_graph/TestPathwayTopologyAnalysis.java
new file mode 100644
index 0000000000000000000000000000000000000000..1fed61424859fe335014e3fc2baaf46f74e30890
--- /dev/null
+++ b/met4j-graph/src/test/java/fr/inrae/toulouse/metexplore/met4j_graph/TestPathwayTopologyAnalysis.java
@@ -0,0 +1,267 @@
+package fr.inrae.toulouse.metexplore.met4j_graph;
+
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.*;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.collection.BioCollection;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.PathwayTopologyAnalysis;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.centrality.EigenVectorCentrality;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.centrality.PathBasedCentrality;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.CompoundGraph;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.ReactionEdge;
+import org.junit.BeforeClass;
+import org.junit.Test;
+
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.Map;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+
+public class TestPathwayTopologyAnalysis {
+
+ public static CompoundGraph toyGraph;
+ public static BioNetwork toyNetwork;
+ public static BioMetabolite a, b, c, d, e, f, h, g;
+ public static BioReaction r1,r2,r3,r4,r5,r6,r7,r8;
+ public static BioPathway x,y,z;
+ public static BioCompartment na;
+ public static PathBasedCentrality<BioMetabolite, ReactionEdge, CompoundGraph> toyMeasure;
+
+ @BeforeClass
+ public static void init() {
+
+ a = new BioMetabolite("a");
+ b = new BioMetabolite("b");
+ c = new BioMetabolite("c");
+ d = new BioMetabolite("d");
+ e = new BioMetabolite("e");
+ f = new BioMetabolite("f");
+ h = new BioMetabolite("h");
+ g = new BioMetabolite("g");
+
+ r1 = new BioReaction("r1");r1.setReversible(true);
+ r2 = new BioReaction("r2");r2.setReversible(true);
+ r3 = new BioReaction("r3");r3.setReversible(true);
+ r4 = new BioReaction("r4");r4.setReversible(true);
+ r5 = new BioReaction("r5");r5.setReversible(true);
+ r6 = new BioReaction("r6");r6.setReversible(true);
+ r7 = new BioReaction("r7");r7.setReversible(true);
+ r8 = new BioReaction("r8");r8.setReversible(true);
+
+ x = new BioPathway("x");
+ y = new BioPathway("y");
+ z = new BioPathway("z");
+
+ na = new BioCompartment("NA");
+
+ ReactionEdge ab = new ReactionEdge(a, b, r1);
+ ReactionEdge ba = new ReactionEdge(b, a, r1);
+ ReactionEdge bc = new ReactionEdge(b, c, r2);
+ ReactionEdge cb = new ReactionEdge(c, b, r2);
+ ReactionEdge cd = new ReactionEdge(c, d, r3);
+ ReactionEdge dc = new ReactionEdge(d, c, r3);
+ ReactionEdge de = new ReactionEdge(d, e, r4);
+ ReactionEdge ed = new ReactionEdge(e, d, r4);
+ ReactionEdge ec = new ReactionEdge(e, c, r5);
+ ReactionEdge ce = new ReactionEdge(c, e, r5);
+ ReactionEdge af = new ReactionEdge(a, f, r6);
+ ReactionEdge fa = new ReactionEdge(f, a, r6);
+ ReactionEdge fg = new ReactionEdge(f, g, r7);
+ ReactionEdge gf = new ReactionEdge(g, f, r7);
+ ReactionEdge ga = new ReactionEdge(g, a, r8);
+ ReactionEdge ag = new ReactionEdge(a, g, r8);
+
+ toyNetwork=new BioNetwork();
+ toyNetwork.add(a,b,c,d,e,f,g);
+ toyNetwork.add(r1,r2,r3,r4,r5,r6,r7,r8);
+ toyNetwork.add(x,y,z);
+ toyNetwork.add(na);
+
+ toyNetwork.affectToCompartment(na,a,b,c,d,e,f,g);
+
+ toyNetwork.affectLeft(r1,1.0,na,a);
+ toyNetwork.affectRight(r1,1.0,na,b);
+ toyNetwork.affectLeft(r2,1.0,na,b);
+ toyNetwork.affectRight(r2,1.0,na,c);
+ toyNetwork.affectLeft(r3,1.0,na,c);
+ toyNetwork.affectRight(r3,1.0,na,d);
+ toyNetwork.affectLeft(r4,1.0,na,d);
+ toyNetwork.affectRight(r4,1.0,na,e);
+ toyNetwork.affectLeft(r5,1.0,na,e);
+ toyNetwork.affectRight(r5,1.0,na,c);
+ toyNetwork.affectLeft(r6,1.0,na,a);
+ toyNetwork.affectRight(r6,1.0,na,f);
+ toyNetwork.affectLeft(r7,1.0,na,f);
+ toyNetwork.affectRight(r7,1.0,na,g);
+ toyNetwork.affectLeft(r8,1.0,na,g);
+ toyNetwork.affectRight(r8,1.0,na,a);
+
+ toyNetwork.affectToPathway(x,r1,r6,r7,r8);
+ toyNetwork.affectToPathway(y,r2);
+ toyNetwork.affectToPathway(z,r3,r4,r5);
+
+ toyGraph = new CompoundGraph();
+ toyGraph.addVertex(a);
+ toyGraph.addVertex(b);
+ toyGraph.addVertex(c);
+ toyGraph.addVertex(d);
+ toyGraph.addVertex(e);
+ toyGraph.addVertex(f);
+ toyGraph.addVertex(g);
+ toyGraph.addEdge(a, b, ab);
+ toyGraph.addEdge(b, c, bc);
+ toyGraph.addEdge(c, d, cd);
+ toyGraph.addEdge(d, e, de);
+ toyGraph.addEdge(e, c, ec);
+ toyGraph.addEdge(a, f, af);
+ toyGraph.addEdge(f, g, fg);
+ toyGraph.addEdge(g, a, ga);
+ toyGraph.addEdge(b, a, ba);
+ toyGraph.addEdge(c, b, cb);
+ toyGraph.addEdge(d, c, dc);
+ toyGraph.addEdge(e, d, ed);
+ toyGraph.addEdge(c, e, ce);
+ toyGraph.addEdge(f, a, fa);
+ toyGraph.addEdge(g, f, gf);
+ toyGraph.addEdge(a, g, ag);
+
+ toyMeasure = new PathBasedCentrality<>(toyGraph);
+ }
+
+ @Test
+ public void testBetweenness() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi);
+
+ Map<BioMetabolite, Integer> toyBetweenness = toyMeasure.getGeodesicBetweenness();
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.betweenness(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(toyBetweenness.get(a)+toyBetweenness.get(b), res.get(x), 0.00000001);
+ assertEquals(toyBetweenness.get(b), res.get(y), 0.00000001);
+ assertEquals(toyBetweenness.get(e), res.get(z), 0.00000001);
+ }
+
+ @Test
+ public void testNormalization() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi).useNormalization();
+
+ Map<BioMetabolite, Integer> toyBetweenness = toyMeasure.getGeodesicBetweenness();
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.betweenness(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+
+ assertEquals(Double.valueOf(toyBetweenness.get(a)+toyBetweenness.get(b)) / Double.valueOf(toyBetweenness.get(a)+toyBetweenness.get(b)+toyBetweenness.get(f)+toyBetweenness.get(g)), res.get(x), 0.00000001);
+ assertEquals(Double.valueOf(toyBetweenness.get(b)) / Double.valueOf(toyBetweenness.get(b)+toyBetweenness.get(c)), res.get(y), 0.00000001);
+ assertEquals(Double.valueOf(toyBetweenness.get(e)) / Double.valueOf(toyBetweenness.get(c)+toyBetweenness.get(d)+toyBetweenness.get(e)), res.get(z), 0.00000001);
+ }
+
+ @Test
+ public void testBetweennessII() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi);
+
+ Map<BioMetabolite, Integer> toyBetweenness = toyMeasure.getGeodesicBetweenness();
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.betweenness(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(toyBetweenness.get(a)+toyBetweenness.get(b), res.get(x), 0.00000001);
+ assertEquals(toyBetweenness.get(b), res.get(y), 0.00000001);
+ assertFalse(res.containsKey(z));
+ }
+
+ @Test
+ public void testOutDegree() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi);
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.outDegree(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(5, res.get(x), 0.00000001);
+ assertEquals(2, res.get(y), 0.00000001);
+ assertEquals(2,res.get(z), 0.00000001);
+ }
+
+ @Test
+ public void testMapConstructor() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ HashMap<BioPathway, Collection<BioMetabolite>> kb = new HashMap<>();
+ BioCollection<BioMetabolite> xCpds = new BioCollection<>();xCpds.add(g,f,a,b);kb.put(x,xCpds);
+ BioCollection<BioMetabolite> yCpds = new BioCollection<>();yCpds.add(b,c);kb.put(y,yCpds);
+ BioCollection<BioMetabolite> zCpds = new BioCollection<>();zCpds.add(c,e,d);kb.put(z,zCpds);
+
+
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(kb,toyGraph,noi);
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.outDegree(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(5, res.get(x), 0.00000001);
+ assertEquals(2, res.get(y), 0.00000001);
+ assertEquals(2,res.get(z), 0.00000001);
+ }
+
+ @Test
+ public void testNeighbours() {
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi);
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.neighbors(),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(5, res.get(x), 0.00000001);
+ assertEquals(2, res.get(y), 0.00000001);
+ assertEquals(2,res.get(z), 0.00000001);
+ }
+
+ @Test
+ public void testPageRank() {
+
+
+ HashSet<BioMetabolite> noi = new HashSet<>();
+ noi.add(a);
+ noi.add(b);
+ noi.add(e);
+ PathwayTopologyAnalysis analysis = new PathwayTopologyAnalysis(toyNetwork,toyGraph,noi);
+
+ EigenVectorCentrality<BioMetabolite,ReactionEdge,CompoundGraph> toyMeasure2 = new EigenVectorCentrality<>(toyGraph);
+ HashMap<String, Double> toyPageRank = toyMeasure2.computePowerMethodPageRank(0.85,15000,0.001);
+
+ Map<BioPathway,Double> res = analysis.run(PathwayTopologyAnalysis.IndividualScoringStrategy.pageRank(0.85,15000,0.001),
+ PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+
+ assertEquals(toyPageRank.get(a.getId())+toyPageRank.get(b.getId()), res.get(x), 0.00000001);
+ assertEquals(toyPageRank.get(b.getId()), res.get(y), 0.00000001);
+ assertEquals(toyPageRank.get(e.getId()), res.get(z), 0.00000001);
+
+ }
+}
diff --git a/met4j-toolbox/src/main/java/fr/inrae/toulouse/metexplore/met4j_toolbox/networkAnalysis/TopologicalPathwayAnalysis.java b/met4j-toolbox/src/main/java/fr/inrae/toulouse/metexplore/met4j_toolbox/networkAnalysis/TopologicalPathwayAnalysis.java
new file mode 100644
index 0000000000000000000000000000000000000000..5b2df5b72036c0117c978fef3b5652b41ebf8eb6
--- /dev/null
+++ b/met4j-toolbox/src/main/java/fr/inrae/toulouse/metexplore/met4j_toolbox/networkAnalysis/TopologicalPathwayAnalysis.java
@@ -0,0 +1,231 @@
+package fr.inrae.toulouse.metexplore.met4j_toolbox.networkAnalysis;
+
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioMetabolite;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioNetwork;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.BioPathway;
+import fr.inrae.toulouse.metexplore.met4j_core.biodata.collection.BioCollection;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.analyze.PathwayTopologyAnalysis;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.connect.weighting.*;
+import fr.inrae.toulouse.metexplore.met4j_graph.computation.transform.VertexContraction;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.WeightingPolicy;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.CompoundGraph;
+import fr.inrae.toulouse.metexplore.met4j_graph.core.compound.ReactionEdge;
+import fr.inrae.toulouse.metexplore.met4j_graph.io.Bionetwork2BioGraph;
+import fr.inrae.toulouse.metexplore.met4j_graph.io.NodeMapping;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.JsbmlReader;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.Met4jSbmlReaderException;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.plugin.FBCParser;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.plugin.GroupPathwayParser;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.plugin.NotesParser;
+import fr.inrae.toulouse.metexplore.met4j_io.jsbml.reader.plugin.PackageParser;
+import fr.inrae.toulouse.metexplore.met4j_toolbox.generic.AbstractMet4jApplication;
+import fr.inrae.toulouse.metexplore.met4j_toolbox.generic.annotations.EnumFormats;
+import fr.inrae.toulouse.metexplore.met4j_toolbox.generic.annotations.EnumParameterTypes;
+import fr.inrae.toulouse.metexplore.met4j_toolbox.generic.annotations.Format;
+import fr.inrae.toulouse.metexplore.met4j_toolbox.generic.annotations.ParameterType;
+import org.kohsuke.args4j.Option;
+
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashSet;
+import java.util.Map;
+
+public class TopologicalPathwayAnalysis extends AbstractMet4jApplication {
+
+ @Format(name= EnumFormats.Sbml)
+ @ParameterType(name= EnumParameterTypes.InputFile)
+ @Option(name = "-s", usage = "input SBML file", required = true)
+ public String inputPath = null;
+
+ @ParameterType(name= EnumParameterTypes.InputFile)
+ @Format(name= EnumFormats.Txt)
+ @Option(name = "-sc", usage = "input Side compound file (recommended)", required = false)
+ public String inputSide = null;
+
+ @ParameterType(name= EnumParameterTypes.OutputFile)
+ @Format(name= EnumFormats.Gml)
+ @Option(name = "-o", usage = "output result file (Gml format)", required = true)
+ public String outputPath = null;
+
+ enum strategy {no, by_name,by_id}
+ @Option(name = "-mc", aliases = {"--mergecomp"}, usage = "merge compartments. " +
+ "Use names if consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form \"xxx_y\" with xxx as base identifier and y as compartment label).")
+ public strategy mergingStrat = strategy.no;
+ public String idRegex = "^(\\w+)_\\w$";
+
+
+ @Option(name = "-ri", aliases = {"--removeIsolatedNodes"}, usage = "remove isolated nodes", required = false)
+ public boolean removeIsolated = false;
+
+ @ParameterType(name=EnumParameterTypes.InputFile)
+ @Format(name=EnumFormats.Tsv)
+ @Option(name = "-cw", aliases = {"--customWeights"}, usage = "an optional file containing weights for compound pairs, taken into account for betweenness computation. Edges not found in file will be removed", forbids = {"-dw"})
+ public String weightFile = null;
+
+ @Option(name = "-un", aliases = {"--undirected"}, usage = "the compound graph built from the metabolic network and used for computations will undirected, i.e. the reaction directions won't be taken into account", required = false)
+ public boolean undirected = false;
+
+
+ @Format(name= EnumFormats.Txt)
+ @ParameterType(name= EnumParameterTypes.InputFile)
+ @Option(name = "-noi", usage = "file containing the list of metabolites of interests (one per line)", required = true)
+ public String dataPath = null;
+
+ @Option(name = "-out", aliases = {"--outDegree"}, usage = "use out-degree as scoring function instead of betweenness (faster computation)", required = false)
+ public boolean out = false;
+
+
+ public static void main(String[] args) {
+
+ TopologicalPathwayAnalysis app = new TopologicalPathwayAnalysis();
+
+ app.parseArguments(args);
+
+ app.run();
+
+ }
+
+
+ public void run() {
+ //open file
+ FileWriter fw = null;
+ try {
+ fw = new FileWriter(outputPath);
+ } catch (IOException e) {
+ System.err.println("Error while opening the output file");
+ System.err.println(e.getMessage());
+ System.exit(1);
+ }
+
+ System.out.print("Reading SBML...");
+ JsbmlReader reader = new JsbmlReader(this.inputPath);
+ ArrayList<PackageParser> pkgs = new ArrayList<>(Arrays.asList(
+ new NotesParser(false), new GroupPathwayParser()));
+
+ BioNetwork network = null;
+
+ try {
+ network = reader.read(pkgs);
+ } catch (Met4jSbmlReaderException e) {
+ System.err.println("Error while reading the SBML file");
+ System.err.println(e.getMessage());
+ System.exit(1);
+ }
+ System.out.println(" Done.\n\n");
+
+
+ System.out.print("Building Network...");
+ Bionetwork2BioGraph builder = new Bionetwork2BioGraph(network);
+ CompoundGraph graph = builder.getCompoundGraph();
+
+ //Graph processing: side compound removal [optional]
+ if (inputSide != null) {
+ System.out.println("Removing side compounds...");
+ NodeMapping<BioMetabolite, ReactionEdge, CompoundGraph> mapper = new NodeMapping<>(graph).skipIfNotFound();
+ BioCollection<BioMetabolite> sideCpds = null;
+ try {
+ sideCpds = mapper.map(inputSide);
+ } catch (IOException e) {
+ System.err.println("Error while reading the side compound file");
+ System.err.println(e.getMessage());
+ System.exit(1);
+ }
+ boolean removed = graph.removeAllVertices(sideCpds);
+ if (removed) System.out.println(sideCpds.size() + " compounds removed.");
+ }
+
+ //Graph processing: set weights [optional]
+ WeightingPolicy<BioMetabolite, ReactionEdge, CompoundGraph> wp = new UnweightedPolicy<>();
+ if (weightFile != null) {
+ System.out.println("Setting edge weights...");
+ wp = new WeightsFromFile(weightFile).removeEdgeNotInFile();
+ wp.setWeight(graph);
+ System.out.println(" Done.");
+ }else{
+ wp.setWeight(graph);
+ }
+
+ //invert graph as undirected (copy edge weight to reversed edge)
+ if(undirected){
+ System.out.print("Create Undirected...");
+ graph.asUndirected();
+ System.out.println(" Done.");
+ }
+
+ //merge compartment
+ if(mergingStrat!=strategy.no){
+ System.out.print("Merging compartments...");
+ VertexContraction vc = new VertexContraction();
+ VertexContraction.Mapper merger = mergingStrat.equals(strategy.by_name) ? new VertexContraction.MapByName() : new VertexContraction.MapByIdSubString(idRegex);
+ graph = vc.decompartmentalize(graph, merger);
+ System.out.println(" Done.");
+ }
+
+ //remove isolated nodes
+ if(removeIsolated){
+ System.out.println("Remove isolated nodes...");
+ HashSet<BioMetabolite> nodes = new HashSet<>(graph.vertexSet());
+ graph.removeIsolatedNodes();
+ nodes.removeAll(graph.vertexSet());
+ for(BioMetabolite n : nodes){
+ System.out.println("\tremoving " + n.getName());
+ }
+ System.out.println(" Done.");
+ }
+
+ System.out.println(" Network successfully created.\n\n");
+
+ System.out.println("Importing nodes of interest");
+ NodeMapping<BioMetabolite, ReactionEdge, CompoundGraph> mapper = new NodeMapping<>(graph).throwErrorIfNotFound();
+ HashSet<BioMetabolite> data = null;
+ try {
+ data = new HashSet<>(mapper.map(dataPath));
+ } catch (IOException e) {
+ System.err.println("Error while reading the source metabolite file");
+ System.err.println(e.getMessage());
+ System.exit(1);
+ }
+ System.out.println("Done.");
+
+
+ System.out.println("Computing Pathway topology Analysis... (may take a while)");
+ PathwayTopologyAnalysis computor = new PathwayTopologyAnalysis(network,graph,data).useNormalization();
+ PathwayTopologyAnalysis.IndividualScoringStrategy strat = out ? PathwayTopologyAnalysis.IndividualScoringStrategy.outDegree() : PathwayTopologyAnalysis.IndividualScoringStrategy.betweenness() ;
+ Map<BioPathway, Double> res = computor.run(strat,PathwayTopologyAnalysis.AggregationStrategy.rawSum());
+ System.out.println("Done.");
+
+ //export results
+ System.out.print("Exporting...");
+ try {
+ for (Map.Entry<BioPathway, Double> e : res.entrySet()) {
+ BioPathway p = e.getKey();
+ fw.write(p.getId() + "\t" + p.getName() + "\t" + e.getValue() + "\n");
+ }
+ fw.close();
+ } catch (IOException e) {
+ System.err.println("Error while writing the result file");
+ System.err.println(e.getMessage());
+ System.exit(1);
+ }
+ System.out.println("Done.");
+ return;
+ }
+
+ @Override
+ public String getLabel() {return this.getClass().getSimpleName();}
+
+ @Override
+ public String getLongDescription() {
+ return "Run a Topological Pathway Analysis (TPA) to identify key pathways based on topological properties of its mapped compounds." +
+ " From a list of compounds of interest, the app compute their betweenness centrality (which quantifies how often a compound acts as a intermediary along the shortest paths between pairs of other compounds in the network," +
+ " which, if high, suggest a critical role in the overall flow within the network). Each pathway is scored according to the summed centrality of its metabolites found in the dataset." +
+ " Alternatively to the betweenness, one can make use of the out-degree (the number of outgoing link, i.e. number of direct metabolic product) as a criterion of importance." +
+ " TPA is complementary to statistical enrichment analysis to ensures a more meaningful interpretation of the data, by taking into account the influence of identified compounds on the structure of the pathways.";
+ }
+
+ @Override
+ public String getShortDescription() {return "Run a Topological Pathway Analysis to identify key pathways based on topological properties of its constituting compounds.";}
+}
+