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1 #!/usr/bin/env python
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2 """
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3 Modified version of code examples from the chemfp project.
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4 http://code.google.com/p/chem-fingerprints/
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5 Thanks to Andrew Dalke of Andrew Dalke Scientific!
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6 """
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7
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8 import chemfp
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9 import sys
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10 import os
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11 import tempfile
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12
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13 temp_file = tempfile.NamedTemporaryFile()
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14 temp_link = "%s.%s" % (temp_file.name, 'fps')
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15 temp_file.close()
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16 os.system('ln -s %s %s' % (os.path.realpath(sys.argv[1]), temp_link) )
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17
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18
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19 chemfp_fingerprint_file = temp_link
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20 tanimoto_threshold = float(sys.argv[2])
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21 outfile = sys.argv[3]
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22 processors = int(sys.argv[4])
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23
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24
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25 def get_hit_indicies(hits):
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26 return [id for (id, score) in hits]
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27
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28 out = open(outfile, 'w')
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29 dataset = chemfp.load_fingerprints( chemfp_fingerprint_file )
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30
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31 chemfp.set_num_threads( processors )
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32 search = dataset.threshold_tanimoto_search_arena(dataset, threshold = tanimoto_threshold)
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33 #search = chemfp.search.threshold_tanimoto_search_symmetric (dataset, threshold = tanimoto_threshold)
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34
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35 # Reorder so the centroid with the most hits comes first.
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36 # (That's why I do a reverse search.)
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37 # Ignore the arbitrariness of breaking ties by fingerprint index
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38 results = sorted( ( (len(hits), i, hits) for (i, hits) in enumerate(search.iter_indices_and_scores()) ),reverse=True)
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39
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40
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41 # Determine the true/false singletons and the clusters
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42 true_singletons = []
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43 false_singletons = []
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44 clusters = []
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45
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46 seen = set()
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47
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48 for (size, fp_idx, hits) in results:
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49 if fp_idx in seen:
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50 # Can't use a centroid which is already assigned
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51 continue
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52 seen.add(fp_idx)
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53 print size, fp_idx, hits
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54 if size == 1:
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55 # The only fingerprint in the exclusion sphere is itself
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56 true_singletons.append(fp_idx)
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57 continue
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58
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59 members = get_hit_indicies(hits)
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60 # Figure out which ones haven't yet been assigned
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61 unassigned = [target_idx for target_idx in members if target_idx not in seen]
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62
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63 if not unassigned:
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64 false_singletons.append(fp_idx)
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65 continue
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66
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67 # this is a new cluster
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68 clusters.append( (fp_idx, unassigned) )
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69 seen.update(unassigned)
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70
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71 len_cluster = len(clusters)
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72 #out.write( "#%s true singletons: %s\n" % ( len(true_singletons), " ".join(sorted(dataset.ids[idx] for idx in true_singletons)) ) )
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73 #out.write( "#%s false singletons: %s\n" % ( len(false_singletons), " ".join(sorted(dataset.ids[idx] for idx in false_singletons)) ) )
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74
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75 out.write( "#%s true singletons\n" % len(true_singletons) )
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76 out.write( "#%s false singletons\n" % len(false_singletons) )
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77 out.write( "#clusters: %s\n" % len_cluster )
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78
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79 # Sort so the cluster with the most compounds comes first,
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80 # then by alphabetically smallest id
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81 def cluster_sort_key(cluster):
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82 centroid_idx, members = cluster
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83 return -len(members), dataset.ids[centroid_idx]
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84
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85 clusters.sort(key=cluster_sort_key)
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86
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87
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88 for centroid_idx, members in clusters:
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89 centroid_name = dataset.ids[centroid_idx]
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90 out.write("%s\t%s\t%s\n" % (centroid_name, len(members), " ".join(sorted(dataset.ids[idx] for idx in members))))
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91 #ToDo: len(members) need to be some biggest top 90% or something ...
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92
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93 for idx in sorted(true_singletons):
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94 out.write("%s\t%s\n" % (dataset.ids[idx], 0))
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95
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96 out.close()
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97 os.remove( temp_link )
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