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bin.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+import numpy as np
+import scipy.sparse as scisp
+from math import log,exp,sqrt
+import logging
+import igraph as ig
+import leidenalg
+from sklearn import silhouette_score
+import os
+
+
+# package logger
+logger = logging.getLogger(__name__)
+
+
+class ClusterBin:
+    def __init__(self, path , viral_info , map_combine , random_seed = 42):
+        '''
+        perc: threshold of spurious contacts
+        viral_info: viral information
+        min_combine: integrative graph
+        '''
+
+        #information of the viral contigs
+        self.path = path
+        self.viral_name = []
+        for i in range(len(viral_info)):
+            temp = viral_info[i]
+            self.viral_name.append(temp.name)
+
+
+        ###########use leiden algorithm to do clustering#########
+        self.map_combine = scisp.coo_matrix(map_combine)
+
+        vcount = self.map_combine.shape[0]
+        sources = self.map_combine.row
+        targets = self.map_combine.col
+        index = sources>targets
+        sources = sources[index]
+        targets = targets[index]
+        edgelist = list(zip(sources, targets))
+        g = ig.Graph(vcount, edgelist)
+
+        SIL_score = []
+        cluster_range = np.arange(2,50)
+        for n_clusters in cluster_range:
+            part = leidenalg.find_partition(g , leidenalg.RBConfigurationVertexPartition , resolution_parameter = n_clusters , n_iterations= -1 , seed = random_seed )
+            part = list(part)
+
+            label_pred = np.ones(self.map_combine.shape[0])
+            for ci in range(len(part)):
+                for contig in part[ci]:
+                    label_pred[contig] = ci
+
+            SIL_score.append(silhouette_score(self.map_combine.todense() , np.array(label_pred)))
+
+        optimal = SIL_score.index(max(SIL_score))
+        part = leidenalg.find_partition(g , leidenalg.RBConfigurationVertexPartition, resolution_parameter = cluster_range[optimal] , n_iterations= -1 , seed = random_seed)
+        part = list(part)
+        logger.info('the number of generated viral bins is {}'.format(len(part)))
+
+        self.dist_cluster = {}
+        for ci in range(len(part)):
+            for id in part[ci]:
+                self.dist_cluster[self.viral_name[id]] = 'group'+str(ci)
+
+        self._write_cluster()
+
+
+
+    def _write_cluster(self):
+        ########create file for checkm################
+        with open(os.path.join(self.path ,'cluster_viral_contig.txt'),'w') as out:
+            for key , value in self.dist_cluster.items():
+                out.write(str(key)+ '\t' +str(value))
+                out.write('\n')