def test01(self):
infile = self.data('clusters.uc')
with open(infile) as f:
cluster_ids, cluster_sizes = sequtils.parse_uc(f)
counter = Counter()
for cluster, count in cluster_sizes.items():
counter[count] += 1
# most of the clusters are singletons
self.assertEquals(counter.most_common(1)[0][0], 1)
def action(args):
if args.clusters:
_, fileExt, = os.path.basename(args.clusters.name).split('.')
if fileExt == 'uc':
clusters = parse_uc(args.clusters)[0]
else:
clusters = {seq: tag for seq, tag in csv.reader(args.clusters)}
by_clusters = lambda s: clusters.get(s.id, s.id)
else:
by_clusters = lambda _: 'all one cluster'
seqs = fastalite(args.fastafile)
seqs = islice(seqs, args.limit)
seqs = sorted(seqs, key=by_clusters)
grouped_seqs = groupby(seqs, key=by_clusters)
chunks = ichunker((group for _, group in grouped_seqs), args.rlefile,
args.min_clust_size, args.max_clust_size)
# calculate consensus for each cluster, then accumulate names of
# each set of identical consensus sequences in `exemplars` (keys
# are the consensus sequences themselves).
exemplars = defaultdict(list)
pool = Pool(processes=args.threads)
for cluster, cons in map(align_and_consensus, enumerate(chunks, start=1)):
exemplars[cons].extend([c.id for c in cluster])
# calculate ratios of reads for the smallest group to each of the
# other groups. outseqs is a list of (weight, consensus, list_of_names)
if args.groups and exemplars:
groups = dict(csv.reader(args.groups))
group_counts = Counter(
groups[name] for name in chain.from_iterable(exemplars.values()))
most_common = group_counts.most_common()
_, least_common = most_common[-1]
weights = {k: float(least_common) / v for k, v in most_common}
outseqs = [(sum(weights[groups[n]] for n in names), cons, names)
for cons, names in exemplars.items()]
else:
outseqs = [(len(names), cons, names)
for cons, names in exemplars.items()]
# write each consensus sequence in descending order of weight
outseqs.sort(reverse=True, key=itemgetter(0))
for i, (weight, cons, names) in enumerate(outseqs, start=1):
name_elements = [
args.name_prefix, 'cons{:04}'.format(i), '{:.0f}'.format(weight),
args.name_suffix
]
consname = args.name_delimiter.join([e for e in name_elements if e])
log.debug('writing {}'.format(consname))
args.outfile.write('>{}\n{}\n'.format(consname, cons))
if args.readmap:
args.readmap.writerows((name, consname) for name in names)
if args.clustermap and args.specimen:
args.clustermap.writerow((consname, args.specimen))
if args.weights:
args.weights.writerow((consname, weight))
def action(args):
if args.clusters:
_, fileExt, = os.path.basename(args.clusters.name).split('.')
if fileExt == 'uc':
clusters = parse_uc(args.clusters)[0]
else:
clusters = {seq: tag for seq,tag in csv.reader(args.clusters)}
by_clusters = lambda s: clusters.get(s.id, s.id)
else:
by_clusters = lambda _: 'all one cluster'
seqs = fastalite(args.fastafile)
seqs = islice(seqs, args.limit)
seqs = sorted(seqs, key = by_clusters)
grouped_seqs = groupby(seqs, key = by_clusters)
chunks = ichunker((group for _, group in grouped_seqs),
args.rlefile, args.min_clust_size, args.max_clust_size)
# calculate consensus for each cluster, then accumulate names of
# each set of identical consensus sequences in `exemplars` (keys
# are the consensus sequences themselves).
exemplars = defaultdict(list)
pool = Pool(processes = args.threads)
for cluster, cons in map(align_and_consensus, enumerate(chunks, start = 1)):
exemplars[cons].extend([c.id for c in cluster])
# calculate ratios of reads for the smallest group to each of the
# other groups. outseqs is a list of (weight, consensus, list_of_names)
if args.groups and exemplars:
groups = dict(csv.reader(args.groups))
group_counts = Counter(groups[name] for name in chain.from_iterable(exemplars.values()))
most_common = group_counts.most_common()
_, least_common = most_common[-1]
weights = {k: float(least_common)/v for k, v in most_common}
outseqs = [(sum(weights[groups[n]] for n in names), cons, names)
for cons, names in exemplars.items()]
else:
outseqs = [(len(names), cons, names) for cons, names in exemplars.items()]
# write each consensus sequence in descending order of weight
outseqs.sort(reverse=True, key=itemgetter(0))
for i, (weight, cons, names) in enumerate(outseqs, start=1):
name_elements = [args.name_prefix,
'cons{:04}'.format(i),
'{:.0f}'.format(weight),
args.name_suffix]
consname = args.name_delimiter.join([e for e in name_elements if e])
log.debug('writing {}'.format(consname))
args.outfile.write('>{}\n{}\n'.format(consname, cons))
if args.readmap:
args.readmap.writerows((name, consname) for name in names)
if args.clustermap and args.specimen:
args.clustermap.writerow((consname, args.specimen))
if args.weights:
args.weights.writerow((consname, weight))
