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Pick as default the clustering method that produce the least amount of clusters #6

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Pick as default the clustering method that produce the least amount of clusters #6

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28 changes: 16 additions & 12 deletions netsyn/netsyn_syntenyFinder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -524,7 +524,7 @@ def run(PROTEINS, TARGETS, GCUSER, GAP, CUTOFF, ADVANCEDSETTINGSFILENAME):

for cluster in range(len(walktrap_clustering)):
for vertex in walktrap_clustering[cluster]:
maxi_graph.vs[vertex]['cluster_WT'] = cluster
maxi_graph.vs[vertex]['cluster_WalkTrap'] = cluster

method_to_clstr2alpha_index["WalkTrap"][cluster] = compute_alpha_index(maxi_graph, cluster_vertexs= walktrap_clustering[cluster])

Expand Down Expand Up @@ -566,7 +566,7 @@ def run(PROTEINS, TARGETS, GCUSER, GAP, CUTOFF, ADVANCEDSETTINGSFILENAME):
for cluster in range(len(graph_infomap)):
for vertex in graph_infomap[cluster]:
maxi_graph.vs[vertex]['cluster_Infomap'] = cluster
method_to_clstr2alpha_index["Infomap"][cluster] = compute_alpha_index(maxi_graph, cluster_vertexs=graph_infomap[cluster])
method_to_clstr2alpha_index["Infomap"][cluster] = compute_alpha_index(maxi_graph, cluster_vertexs=graph_infomap[cluster])

# ### Leading EigenVector Clustering
# # donne le même résultat que WalkTrap (sur données UniProtAC, pas avec BKACE !)
Expand Down Expand Up @@ -611,11 +611,23 @@ def run(PROTEINS, TARGETS, GCUSER, GAP, CUTOFF, ADVANCEDSETTINGSFILENAME):
targetsNumber = len(targets_info)
targets_info, prots_info = proteinsRemoval(
prots_info, targets_info, maxi_graph)

# Determine clustering order. Clustering methods with less clusters are considered first
# If equal order is WalkTrap > Louvain > Infomap > MCL
method_to_cluster_count = {"WalkTrap": len(walktrap_clustering),
"Louvain": len(graph_louvain),
"Infomap": len(graph_infomap),
"MCL": len(clusters),
}
clustering_methods_sorted = sorted(method_to_cluster_count, key=method_to_cluster_count.get)
list_of_nodes = []
for target_node in maxi_graph.vs:
target_idx = int(target_node['name'])
protein_idx = int(targets_info[target_idx]['protein_idx'])

# Create clusterings dict in the sorted order
clusterings_ordered = {f'{method}': maxi_graph.vs[target_node.index][f'cluster_{method}']
for method in clustering_methods_sorted}

dico = {'protein_idx': protein_idx,
'id': prots_info[protein_idx]['id'],
'UniProt_AC': prots_info[protein_idx]['UniProt_AC'],
Expand All @@ -624,15 +636,7 @@ def run(PROTEINS, TARGETS, GCUSER, GAP, CUTOFF, ADVANCEDSETTINGSFILENAME):
'context_idx': targets_info[target_idx]['context_idx'],
'organism_id': targets_info[target_idx]['organism_id'],
'organism_idx': targets_info[target_idx]['organism_idx'],
'clusterings': {'WalkTrap':
maxi_graph.vs[target_node.index]['cluster_WT'],
'Louvain':
maxi_graph.vs[target_node.index]['cluster_Louvain'],
'Infomap':
maxi_graph.vs[target_node.index]['cluster_Infomap'],
'MCL':
maxi_graph.vs[target_node.index]['cluster_MCL']
},
'clusterings': clusterings_ordered,
'families': targets_info[target_idx]['families'],
'Size': 1,
}
Expand Down