def action(args):
seqs = fastalite(args.fasta)
pairs = list(all_pairwise(seqs))
if args.distance:
pairs = [(q, t, 1 - i) for q, t, i in pairs]
if args.split_info and args.matrix_out:
primary, secondary = args.primary_group, args.secondary_group
split_info = list(csv.DictReader(args.split_info))
info = {r['seqname']: r for r in split_info if r['seqname']}
tax = {r['tax_id']:r for r in split_info}
pairs += map(itemgetter(1,0,2), pairs)
def group(seqname):
i = info[seqname]
return i[primary] or i[secondary] if secondary else i[primary]
pairs = ((group(left), group(right), score) for left,right,score in pairs)
# sort and group rows
pairs = list(groupbyl(pairs, key = itemgetter(0)))
matrix_out = csv.writer(args.matrix_out)
# this is the tax_id order we will be using for columns
tax_ids = map(itemgetter(0), pairs)
# get the species names to output as first row
matrix_out.writerow([''] + [tax[t]['tax_name'] for t in tax_ids])
# iterator through the sorted rows (pairs)
for row_id, columns in pairs:
# sort and group columns
columns = dict(groupbyl(columns, key = itemgetter(1)))
# get the species name
row = [tax[row_id]['tax_name']]
for t in tax_ids:
# if t not in columns that means there is only
# sequence representing the group
# therefore the median destance is 0
if t not in columns:
med = 0
else:
col = columns[t]
med = median(map(itemgetter(2), col))
# percent and round
med = math.ceil(med * 100) / 100
row.append(med)
matrix_out.writerow(row)
else:
writer = csv.writer(args.out)
writer.writerow(['query', 'target', 'identity'])
writer.writerows(pairs)
def condense(queries, floor_rank, max_size, ranks, rank_thresholds, target_rank = None):
target_rank = target_rank or ranks[0]
groups = list(groupbyl(queries, key = itemgetter(target_rank)))
num_groups = len(groups)
if rank_thresholds.get(target_rank, max_size) < num_groups:
return queries
# assign where available target_rank_ids
# groups without 'i' values remain assigned at previous (higher) rank
for g in (g for i,g in groups if i):
for q in g:
q['target_rank_id'] = q[target_rank]
# return if we hit the floor
if target_rank == floor_rank:
return queries
# else move down a rank
target_rank = ranks[ranks.index(target_rank) + 1]
# recurse down the tax tree
condensed = []
for _,g in groups:
c = condense(g, floor_rank, max_size, ranks, rank_thresholds, target_rank)
condensed.extend(c)
return condensed
def action(args):
rows = csv.DictReader(args.clusters, delimiter='\t', fieldnames=UCLUST_HEADERS)
grouped = groupbyl(get_mapping(rows), key=itemgetter(0)) # group by centroid
clusters = {c: rows for c, rows in grouped if len(rows) >= args.min_clust_size}
readmap = csv.writer(args.out)
specimenmap = csv.writer(args.specimenmap) \
if (args.specimenmap and args.specimen) else None
weights = csv.writer(args.weights) if args.weights else None
# Calculate ratios of reads for the smallest group to each of the
# other groups.
if args.groups:
groups = dict(csv.reader(args.groups))
most_common = Counter(groups.values()).most_common()
_, least_common = most_common[-1]
wdict = {k: float(least_common) / v for k, v in most_common}
for centroid, cluster in clusters.iteritems():
log.info('writing {}'.format(centroid))
for _centroid, read in cluster:
readmap.writerow([read, centroid])
if specimenmap:
specimenmap.writerow((centroid, args.specimen))
if weights:
if args.groups:
# normalize weight of each cluster by contribution of
# reads by each group defined in --groups
weights.writerow((centroid, sum(wdict[groups[r]] for c, r in cluster)))
else:
weights.writerow((centroid, len(cluster)))
# filter non centroid seqs
if args.fasta_in and args.fasta_out:
for c in (c for c in args.fasta_in if c.id in clusters):
args.fasta_out.write('>{}\n{}\n'.format(c.description, c.seq))
def condense(queries,
floor_rank,
max_size,
ranks,
rank_thresholds,
target_rank=None):
target_rank = target_rank or ranks[0]
groups = list(groupbyl(queries, key=itemgetter(target_rank)))
num_groups = len(groups)
if rank_thresholds.get(target_rank, max_size) < num_groups:
return queries
# assign where available target_rank_ids
# groups without 'i' values remain assigned at previous (higher) rank
for g in (g for i, g in groups if i):
for q in g:
q['target_rank_id'] = q[target_rank]
# return if we hit the floor
if target_rank == floor_rank:
return queries
# else move down a rank
target_rank = ranks[ranks.index(target_rank) + 1]
# recurse down the tax tree
condensed = []
for _, g in groups:
c = condense(g, floor_rank, max_size, ranks, rank_thresholds,
target_rank)
condensed.extend(c)
return condensed
def action(args):
### format format blast data and add additional available information
fieldnames = None if args.has_header else sequtils.BLAST_HEADER_DEFAULT
blast_results = DictReader(args.blast_file, fieldnames=fieldnames)
blast_results = list(blast_results)
sseqids = set(s['sseqid'] for s in blast_results)
qseqids = set(s['qseqid'] for s in blast_results)
# load seq_info and map file
mapfile = DictReader(args.map, fieldnames=['name', 'specimen'])
mapfile = {
m['name']: m['specimen']
for m in mapfile if m['name'] in qseqids
}
seq_info = DictReader(args.seq_info)
seq_info = {s['seqname']: s for s in seq_info if s['seqname'] in sseqids}
# pident
def pident(b):
return dict(b, pident=float(b['pident'])) if b['sseqid'] else b
blast_results = (pident(b) for b in blast_results)
# coverage
def cov(b):
if b['sseqid'] and b['qcovs']:
b['coverage'] = float(b['qcovs'])
return b
elif b['sseqid']:
c = coverage(b['qstart'], b['qend'], b['qlen'])
return dict(b, coverage=c)
else:
return b
blast_results = (cov(b) for b in blast_results)
# seq info
def info(b):
return dict(seq_info[b['sseqid']], **b) if b['sseqid'] else b
blast_results = (info(b) for b in blast_results)
# tax info
def tax_info(b):
return dict(args.taxonomy[b['tax_id']], **b) if b['sseqid'] else b
blast_results = (tax_info(b) for b in blast_results)
### output file headers
fieldnames = [
'specimen', 'max_percent', 'min_percent', 'max_coverage',
'min_coverage', 'assignment_id', 'assignment'
]
if args.weights:
weights = DictReader(args.weights, fieldnames=['name', 'weight'])
weights = {
d['name']: d['weight']
for d in weights if d['name'] in qseqids
}
fieldnames += ['clusters', 'reads', 'pct_reads']
else:
weights = {}
if args.copy_numbers:
copy_numbers = DictReader(args.copy_numbers)
copy_numbers = {d['tax_id']: float(d['median']) for d in copy_numbers}
fieldnames += ['corrected', 'pct_corrected']
else:
copy_numbers = {}
# TODO: take out target_rank, hi, low and provide in pipeline using csvmod
# TODO: option to include tax_ids (default no)
fieldnames += ['target_rank', 'hi', 'low', 'tax_ids']
### Columns
out = DictWriter(args.out, extrasaction='ignore', fieldnames=fieldnames)
out.writeheader()
if args.out_detail:
args.out_detail.writeheader()
def blast_hit(hit, args):
return hit['sseqid'] and \
hit[args.target_rank] and \
hit['coverage'] >= args.coverage and \
float(weights.get(hit['qseqid'], 1)) >= args.min_cluster_size and \
hit[args.target_rank] not in args.exclude_by_taxid and \
hit['qseqid'] != hit['sseqid'] and \
int(hit['ambig_count']) <= args.max_ambiguous
### Rows
etc = '[no blast result]' # This row will hold all unmatched
# groups have list position prioritization
groups = [
('> {}%'.format(args.max_identity),
lambda h: blast_hit(h, args) and h['pident'] > args.max_identity),
(None, lambda h: blast_hit(h, args) and args.max_identity >= h[
'pident'] > args.min_identity),
('<= {}%'.format(args.min_identity),
lambda h: blast_hit(h, args) and h['pident'] <= args.min_identity),
]
# used later for results output
group_cats = map(itemgetter(0), groups)
group_cats.append(etc)
# assignment rank thresholds
rank_thresholds = (d.split(':') for d in args.group_def)
rank_thresholds = dict((k, int(v)) for k, v in rank_thresholds)
# rt = {k: int(v) for k, v in (d.split(':') for d in args.group_def)}
# group by specimen
if args.map:
specimen_grouper = lambda s: mapfile[s['qseqid']]
elif args.all_one_group:
specimen_grouper = lambda s: args.group_label
else:
specimen_grouper = lambda s: s['qseqid']
blast_results = groupbyl(blast_results, key=specimen_grouper)
assignments = [] # assignment list for assignment ids
for specimen, hits in blast_results:
categories = defaultdict(list)
# clusters will hold the query ids as hits are matched to categories
clusters = set()
# filter out categories
for cat, fltr in groups:
matches = filter(fltr, hits)
if cat:
categories[cat] = matches
else:
# create sets of tax_rank_id
query_group = groupbyl(matches, key=itemgetter('qseqid'))
target_cats = defaultdict(list)
for _, queries in query_group:
queries = condense(queries, args.target_rank,
args.target_max_group_size,
sequtils.RANKS, rank_thresholds)
cat = map(itemgetter('target_rank_id'), queries)
cat = frozenset(cat)
target_cats[cat].extend(queries)
categories = dict(categories, **target_cats)
# add query ids that were matched to a filter
clusters |= set(map(itemgetter('qseqid'), matches))
# remove all hits corresponding to a matched query id (cluster)
hits = filter(lambda h: h['qseqid'] not in clusters, hits)
# remaining hits go in the etc ('no match') category
categories[etc] = hits
# calculate read counts
read_counts = dict()
for k, v in categories.items():
qseqids = set(map(itemgetter('qseqid'), v))
weight = sum(float(weights.get(q, 1)) for q in qseqids)
read_counts[k] = weight
taxids = set()
for k, v in categories.items():
if k is not etc:
for h in v:
taxids.add(h['tax_id'])
### list of assigned ids for count corrections
assigned_ids = dict()
for k, v in categories.items():
if k is not etc and v:
assigned_ids[k] = set(map(itemgetter('tax_id'), v))
# correction counts
corrected_counts = dict()
for k, v in categories.items():
if k is not etc and v:
av = mean(copy_numbers.get(t, 1) for t in assigned_ids[k])
corrected_counts[k] = ceil(read_counts[k] / av)
# finally take the root value for the etc category
corrected_counts[etc] = ceil(read_counts[etc] /
copy_numbers.get('1', 1))
# totals for percent calculations later
total_reads = sum(v for v in read_counts.values())
total_corrected = sum(v for v in corrected_counts.values())
# Print classifications per specimen sorted by # of reads in reverse (descending) order
sort_by_reads_assign = lambda (c, h): corrected_counts.get(c, None)
for cat, hits in sorted(categories.items(),
key=sort_by_reads_assign,
reverse=True):
# continue if their are hits
if hits:
# for incrementing assignment id's
if cat not in assignments:
assignments.append(cat)
assignment_id = assignments.index(cat)
reads = read_counts[cat]
reads_corrected = corrected_counts[cat]
clusters = set(map(itemgetter('qseqid'), hits))
results = dict(
hi=args.max_identity,
low=args.min_identity,
target_rank=args.target_rank,
specimen=specimen,
assignment_id=assignment_id,
reads=int(reads),
pct_reads='{0:.2f}'.format(reads / total_reads * 100),
corrected=int(reads_corrected),
pct_corrected='{0:.2f}'.format(reads_corrected /
total_corrected * 100),
clusters=len(clusters))
if cat is etc:
assignment = etc
results = dict(results, assignment=assignment)
else:
taxids = set(map(itemgetter('tax_id'), hits))
coverages = set(map(itemgetter('coverage'), hits))
percents = set(map(itemgetter('pident'), hits))
if cat in group_cats:
assignment = cat
else:
names = [
args.taxonomy[h['target_rank_id']]['tax_name']
for h in hits
]
selectors = [
h['pident'] >= args.asterisk for h in hits
]
assignment = sequtils.format_taxonomy(
names, selectors, '*')
results = dict(
results,
assignment=assignment,
max_percent='{0:.2f}'.format(max(percents)),
min_percent='{0:.2f}'.format(min(percents)),
max_coverage='{0:.2f}'.format(max(coverages)),
min_coverage='{0:.2f}'.format(min(coverages)),
tax_ids=' '.join(taxids))
out.writerow(results)
if args.out_detail:
if not args.details_full:
# drop the no_hits
hits = [h for h in hits if 'tax_id' in h]
# only report heaviest centroid
clusters_and_sizes = [(float(weights.get(c, 1.0)), c)
for c in clusters]
_, largest = max(clusters_and_sizes)
hits = (h for h in hits if h['qseqid'] == largest)
for h in hits:
args.out_detail.writerow(
dict(specimen=specimen,
assignment=assignment,
assignment_id=assignment_id,
hi=args.max_identity,
low=args.min_identity,
target_rank=args.target_rank,
**h))
def action(args):
### format format blast data and add additional available information
fieldnames = None if args.has_header else sequtils.BLAST_HEADER_DEFAULT
blast_results = DictReader(args.blast_file, fieldnames = fieldnames)
blast_results = list(blast_results)
sseqids = set(s['sseqid'] for s in blast_results)
qseqids = set(s['qseqid'] for s in blast_results)
# load seq_info and map file
mapfile = DictReader(args.map, fieldnames = ['name', 'specimen'])
mapfile = {m['name']:m['specimen'] for m in mapfile if m['name'] in qseqids}
seq_info = DictReader(args.seq_info)
seq_info = {s['seqname']:s for s in seq_info if s['seqname'] in sseqids}
# pident
def pident(b):
return dict(b, pident = float(b['pident'])) if b['sseqid'] else b
blast_results = (pident(b) for b in blast_results)
# coverage
def cov(b):
if b['sseqid'] and b['qcovs']:
b['coverage'] = float(b['qcovs'])
return b
elif b['sseqid']:
c = coverage(b['qstart'], b['qend'], b['qlen'])
return dict(b, coverage = c)
else:
return b
blast_results = (cov(b) for b in blast_results)
# seq info
def info(b):
return dict(seq_info[b['sseqid']], **b) if b['sseqid'] else b
blast_results = (info(b) for b in blast_results)
# tax info
def tax_info(b):
return dict(args.taxonomy[b['tax_id']], **b) if b['sseqid'] else b
blast_results = (tax_info(b) for b in blast_results)
### output file headers
fieldnames = ['specimen', 'max_percent', 'min_percent', 'max_coverage',
'min_coverage', 'assignment_id', 'assignment']
if args.weights:
weights = DictReader(args.weights, fieldnames = ['name', 'weight'])
weights = {d['name']:d['weight'] for d in weights if d['name'] in qseqids}
fieldnames += ['clusters', 'reads', 'pct_reads']
else:
weights = {}
if args.copy_numbers:
copy_numbers = DictReader(args.copy_numbers)
copy_numbers = {d['tax_id']:float(d['median']) for d in copy_numbers}
fieldnames += ['corrected', 'pct_corrected']
else:
copy_numbers = {}
# TODO: take out target_rank, hi, low and provide in pipeline using csvmod
# TODO: option to include tax_ids (default no)
fieldnames += ['target_rank', 'hi', 'low', 'tax_ids']
### Columns
out = DictWriter(args.out,
extrasaction = 'ignore',
fieldnames = fieldnames)
out.writeheader()
if args.out_detail:
args.out_detail.writeheader()
def blast_hit(hit, args):
return hit['sseqid'] and \
hit[args.target_rank] and \
hit['coverage'] >= args.coverage and \
float(weights.get(hit['qseqid'], 1)) >= args.min_cluster_size and \
hit[args.target_rank] not in args.exclude_by_taxid and \
hit['qseqid'] != hit['sseqid'] and \
int(hit['ambig_count']) <= args.max_ambiguous
### Rows
etc = '[no blast result]' # This row will hold all unmatched
# groups have list position prioritization
groups = [
('> {}%'.format(args.max_identity),
lambda h: blast_hit(h, args) and h['pident'] > args.max_identity),
(None,
lambda h: blast_hit(h, args) and args.max_identity >= h['pident'] > args.min_identity),
('<= {}%'.format(args.min_identity),
lambda h: blast_hit(h, args) and h['pident'] <= args.min_identity),
]
# used later for results output
group_cats = map(itemgetter(0), groups)
group_cats.append(etc)
# assignment rank thresholds
rank_thresholds = (d.split(':') for d in args.group_def)
rank_thresholds = dict((k, int(v)) for k,v in rank_thresholds)
# rt = {k: int(v) for k, v in (d.split(':') for d in args.group_def)}
# group by specimen
if args.map:
specimen_grouper = lambda s: mapfile[s['qseqid']]
elif args.all_one_group:
specimen_grouper = lambda s: args.group_label
else:
specimen_grouper = lambda s: s['qseqid']
blast_results = groupbyl(blast_results, key = specimen_grouper)
assignments = [] # assignment list for assignment ids
for specimen, hits in blast_results:
categories = defaultdict(list)
# clusters will hold the query ids as hits are matched to categories
clusters = set()
# filter out categories
for cat, fltr in groups:
matches = filter(fltr, hits)
if cat:
categories[cat] = matches
else:
# create sets of tax_rank_id
query_group = groupbyl(matches, key = itemgetter('qseqid'))
target_cats = defaultdict(list)
for _,queries in query_group:
queries = condense(
queries,
args.target_rank,
args.target_max_group_size,
sequtils.RANKS,
rank_thresholds)
cat = map(itemgetter('target_rank_id'), queries)
cat = frozenset(cat)
target_cats[cat].extend(queries)
categories = dict(categories, **target_cats)
# add query ids that were matched to a filter
clusters |= set(map(itemgetter('qseqid'), matches))
# remove all hits corresponding to a matched query id (cluster)
hits = filter(lambda h: h['qseqid'] not in clusters, hits)
# remaining hits go in the etc ('no match') category
categories[etc] = hits
# calculate read counts
read_counts = dict()
for k,v in categories.items():
qseqids = set(map(itemgetter('qseqid'), v))
weight = sum(float(weights.get(q, 1)) for q in qseqids)
read_counts[k] = weight
taxids = set()
for k,v in categories.items():
if k is not etc:
for h in v:
taxids.add(h['tax_id'])
### list of assigned ids for count corrections
assigned_ids = dict()
for k,v in categories.items():
if k is not etc and v:
assigned_ids[k] = set(map(itemgetter('tax_id'), v))
# correction counts
corrected_counts = dict()
for k,v in categories.items():
if k is not etc and v:
av = mean(copy_numbers.get(t, 1) for t in assigned_ids[k])
corrected_counts[k] = ceil(read_counts[k] / av)
# finally take the root value for the etc category
corrected_counts[etc] = ceil(read_counts[etc] / copy_numbers.get('1', 1))
# totals for percent calculations later
total_reads = sum(v for v in read_counts.values())
total_corrected = sum(v for v in corrected_counts.values())
# Print classifications per specimen sorted by # of reads in reverse (descending) order
sort_by_reads_assign = lambda (c,h): corrected_counts.get(c, None)
for cat, hits in sorted(categories.items(), key = sort_by_reads_assign, reverse = True):
# continue if their are hits
if hits:
# for incrementing assignment id's
if cat not in assignments:
assignments.append(cat)
assignment_id = assignments.index(cat)
reads = read_counts[cat]
reads_corrected = corrected_counts[cat]
clusters = set(map(itemgetter('qseqid'), hits))
results = dict(
hi = args.max_identity,
low = args.min_identity,
target_rank = args.target_rank,
specimen = specimen,
assignment_id = assignment_id,
reads = int(reads),
pct_reads = '{0:.2f}'.format(reads / total_reads * 100),
corrected = int(reads_corrected),
pct_corrected = '{0:.2f}'.format(reads_corrected / total_corrected * 100),
clusters = len(clusters))
if cat is etc:
assignment = etc
results = dict(results, assignment = assignment)
else:
taxids = set(map(itemgetter('tax_id'), hits))
coverages = set(map(itemgetter('coverage'), hits))
percents = set(map(itemgetter('pident'), hits))
if cat in group_cats:
assignment = cat
else:
names = [args.taxonomy[h['target_rank_id']]['tax_name'] for h in hits]
selectors = [h['pident'] >= args.asterisk for h in hits]
assignment = sequtils.format_taxonomy(names, selectors, '*')
results = dict(results,
assignment = assignment,
max_percent = '{0:.2f}'.format(max(percents)),
min_percent = '{0:.2f}'.format(min(percents)),
max_coverage = '{0:.2f}'.format(max(coverages)),
min_coverage = '{0:.2f}'.format(min(coverages)),
tax_ids = ' '.join(taxids))
out.writerow(results)
if args.out_detail:
if not args.details_full:
# drop the no_hits
hits = [h for h in hits if 'tax_id' in h]
# only report heaviest centroid
clusters_and_sizes = [(float(weights.get(c, 1.0)), c) for c in clusters]
_, largest = max(clusters_and_sizes)
hits = (h for h in hits if h['qseqid'] == largest)
for h in hits:
args.out_detail.writerow(dict(
specimen = specimen,
assignment = assignment,
assignment_id = assignment_id,
hi = args.max_identity,
low = args.min_identity,
target_rank = args.target_rank,
**h))
