Mercurial > repos > bcclaywell > argo_navis

annotate bin/deme_downsample.py @ 0:d67268158946 draft

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planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
author bcclaywell
date Mon, 12 Oct 2015 17:43:33 -0400
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1 #!/usr/bin/env python
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2 """Given the clustering results of a run of alnclst, this tool takes those results and find a single
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3 representative sequence for each cluster. In particular, it chooses the cluster representative closest to the
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4 cluster center.
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5 """
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6
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7 import argparse
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8 import random
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9 import alnclst
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10 import csv
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11 from Bio import SeqIO
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12
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13
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14 settings = dict(consensus_threshold=None, batches=2, max_iters=100)
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16
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17 def kmeans_runner(seqrecords, k):
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18 "Runs kmeans on seqrecords and picks representatives from each cluster, returning their names in a list."
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19 # Define clustering function we'll run batches number of times
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20 def clustering():
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21 return alnclst.KMeansClsutering(seqrecords, k, settings['consensus_threshold'], max_iters=settings['max_iters'])
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22 # Run the batches, and pick the one with the best convergence
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23 _, clusts = min((c.average_distance(), c) for c in (clustering() for i in
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24 xrange(settings['batches'])))
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25 # Pick the best representative for every cluster, and thow in clust_reps dict
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26 clust_reps = dict()
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27 for cluster_id, sequence, distance in clusts.mapping_iterator():
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28 current = (distance, sequence)
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29 clst_min = clust_reps.get(cluster_id, current)
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30 if current <= clst_min:
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31 clust_reps[cluster_id] = current
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32 return [seqname for (_, (_, seqname)) in clust_reps.iteritems()]
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33
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34
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35 def random_runner(seqnames, k):
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36 "Randomly samples k seqnames from seqnames"
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37 if len(seqnames) < k:
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38 return seqnames
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39 else:
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40 return random.sample(seqnames, k)
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41
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42
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43 def make_deme_map(deme_spec, deme_col):
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44 "Turns deme metadata into a map of deme -> sequence names"
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45 deme_map = dict()
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46 for row in deme_spec:
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47 try:
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48 deme = row[deme_col]
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49 except KeyError:
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50 raise KeyError, "Make sure to specify a --deme-col that's actually in the deme file"
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51 seqname = row['sequence']
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52 try:
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53 deme_map[deme].append(seqname)
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54 except KeyError:
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55 deme_map[deme] = [seqname]
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56 return deme_map
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57
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58
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59 def get_args():
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60 parser = argparse.ArgumentParser(description=__doc__)
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61 parser.add_argument('alignment', type=argparse.FileType('r'), help="Alignment FASTA file")
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62 parser.add_argument('demes', type=argparse.FileType('r'), help="CSV metadata specifying deme info")
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63 parser.add_argument('-c', '--deme-col', default='deme', help="Column specifying 'deme' argument in demes spec")
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64 parser.add_argument('-k', help="Maximum number of representatives for each deme", type=int)
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65 parser.add_argument('-s', '--seed', help="Random seed for reproducibility")
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66 parser.add_argument('-m', '--method', choices=('random', 'kmeans'),
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67 help="Which downsampling method should be used")
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68 parser.add_argument('out_alignment', type=argparse.FileType('w'), help="Downsampled alignment output")
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69 parser.add_argument('out_demes', type=argparse.FileType('w'), help="Downsampled metadata output")
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70 return parser.parse_args()
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71
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72
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73 def main():
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74 args = get_args()
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75 # Set random seed if needed
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76 if args.seed:
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77 random.seed(args.seed)
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78
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79 # Create a lit of seqrecords to make things easier for ourselves
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80 seqrecords = SeqIO.to_dict(SeqIO.parse(args.alignment, 'fasta'))
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81 demes = list(csv.DictReader(args.demes))
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82
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83 # Turn our metadata into a map of deme -> seqnames
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84 deme_map = make_deme_map(demes, args.deme_col)
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85
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86 # Run the specified downsampling method for each deme, and gather kept representatives
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87 rep_seqnames = []
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88 for deme, seqnames in deme_map.iteritems():
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89 # this makes it safe to have a csv file with "extra" stuff
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90 seqnames = [n for n in seqnames if n in seqrecords.keys()]
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91 if args.method == 'random':
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92 deme_rep_seqnames = random_runner(seqnames, args.k)
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93 else:
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94 deme_sequences = [seqrecords[n] for n in seqnames]
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95 deme_rep_seqnames = kmeans_runner(deme_sequences, args.k)
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96 rep_seqnames += deme_rep_seqnames
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97
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98 # Filter down the actual data based on representative names
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99 deme_rep_seqs = [seqrecords[n] for n in rep_seqnames]
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100 deme_rep_meta = [r for r in demes if r['sequence'] in rep_seqnames]
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101
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102 out_demes = csv.DictWriter(args.out_demes, deme_rep_meta[1].keys())
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103 out_demes.writeheader()
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104 out_demes.writerows(deme_rep_meta)
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105
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106 SeqIO.write(deme_rep_seqs, args.out_alignment, 'fasta')
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107
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108 for fh in [args.alignment, args.demes, args.out_alignment, args.out_demes]:
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109 fh.close()
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110
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111
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112 if __name__ == '__main__':
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113 main()
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114
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115