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#!/usr/bin/env python3
"""
Assign Ig sequences into clones
"""
# Info
__author__ = 'Namita Gupta, Jason Anthony Vander Heiden, Gur Yaari, Mohamed Uduman'
from changeo import __version__, __date__
# Imports
import os
import re
import sys
from argparse import ArgumentParser
from collections import OrderedDict
from itertools import chain
from textwrap import dedent
from time import time
from Bio.Seq import translate
# Presto and changeo imports
from presto.Defaults import default_out_args
from presto.IO import printLog, printProgress, printCount, printWarning, printError
from presto.Multiprocessing import manageProcesses
from changeo.Defaults import default_format, default_v_field, default_j_field, default_junction_field
from changeo.Commandline import CommonHelpFormatter, checkArgs, getCommonArgParser, parseCommonArgs
from changeo.Distance import distance_models, calcDistances, formClusters
from changeo.IO import countDbFile, getDbFields, getFormatOperators, getOutputHandle, \
AIRRWriter, ChangeoWriter, checkFields
from changeo.Multiprocessing import DbResult, feedDbQueue, processDbQueue
# Defaults
default_translate = False
default_distance = 0.0
default_index_mode = 'gene'
default_index_action = 'set'
default_distance_model = 'ham'
default_norm = 'len'
default_sym = 'avg'
default_linkage = 'single'
default_max_missing=0
choices_distance_model = ('ham', 'aa', 'hh_s1f', 'hh_s5f',
'mk_rs1nf', 'mk_rs5nf',
'hs1f_compat', 'm1n_compat')
def filterMissing(data, seq_field=default_junction_field, v_field=default_v_field,
j_field=default_j_field, max_missing=default_max_missing):
"""
Splits a set of sequence into passed and failed groups based on the number
of missing characters in the sequence
Arguments:
data : changeo.Multiprocessing.DbData object.
seq_field : sequence field to filter on.
v_field : field containing the V call.
j_field : field containing the J call.
max_missing : maximum number of missing characters (non-ACGT) to permit before failing the record.
Returns:
changeo.Multiprocessing.DbResult : objected containing filtered records.
"""
# Function to validate the sequence string
def _pass(seq):
if len(seq) > 0 and len(re.findall(r'[^ACGT]', seq)) <= max_missing:
return True
else:
return False
# Define result object for iteration and get data records
result = DbResult(data.id, data.data)
if not data:
result.data_pass = []
result.data_fail = data.data
return result
result.data_pass = []
result.data_fail = []
for rec in data.data:
seq = rec.getField(seq_field)
if _pass(seq): result.data_pass.append(rec)
else: result.data_fail.append(rec)
# Add V(D)J to log
result.log['ID'] = ','.join([str(x) for x in data.id])
result.log['VCALL'] = ','.join(set([(r.getVAllele(field=v_field) or '') for r in data.data]))
result.log['JCALL'] = ','.join(set([(r.getJAllele(field=j_field) or '') for r in data.data]))
result.log['JUNCLEN'] = ','.join(set([(str(len(r.junction)) or '0') for r in data.data]))
result.log['CLONED'] = len(result.data_pass)
result.log['FILTERED'] = len(result.data_fail)
return result
def indexByIdentity(index, key, rec, group_fields=None):
"""
Updates a preclone index with a simple key
Arguments:
index : preclone index from groupByGene
key : index key
rec : Receptor to add to the index
group_fields : additional annotation fields to use to group preclones;
if None use only V, J and junction length
Returns:
None : Updates index with new key and records.
"""
index.setdefault(tuple(key), []).append(rec)
def indexByUnion(index, key, rec, group_fields=None):
"""
Updates a preclone index with the union of nested keys
Arguments:
index : preclone index from groupByGene
key : index key
rec : Receptor to add to the index
group_fields : additional annotation fields to use to group preclones;
if None use only V, J and junction length
Returns:
None : Updates index with new key and records.
"""
# List of values for this/new key
val = [rec]
f_range = list(range(2, 3 + (len(group_fields) if group_fields else 0)))
# See if field/junction length combination exists in index
outer_dict = index
for field in f_range:
try:
outer_dict = outer_dict[key[field]]
except KeyError:
outer_dict = None
break
# If field combination exists, look through Js
j_matches = []
if outer_dict is not None:
for j in outer_dict.keys():
if not set(key[1]).isdisjoint(set(j)):
key[1] = tuple(set(key[1]).union(set(j)))
j_matches += [j]
# If J overlap exists, look through Vs for each J
for j in j_matches:
v_matches = []
# Collect V matches for this J
for v in outer_dict[j].keys():
if not set(key[0]).isdisjoint(set(v)):
key[0] = tuple(set(key[0]).union(set(v)))
v_matches += [v]
# If there are V overlaps for this J, pop them out
if v_matches:
val += list(chain(*(outer_dict[j].pop(v) for v in v_matches)))
# If the J dict is now empty, remove it
if not outer_dict[j]:
outer_dict.pop(j, None)
# Add value(s) into index nested dictionary
# OMG Python pointers are the best!
# Add field dictionaries into index
outer_dict = index
for field in f_range:
outer_dict.setdefault(key[field], {})
outer_dict = outer_dict[key[field]]
# Add J, then V into index
if key[1] in outer_dict:
outer_dict[key[1]].update({key[0]: val})
else:
outer_dict[key[1]] = {key[0]: val}
def groupByGene(db_iter, group_fields=None, v_field=default_v_field, j_field=default_j_field,
mode=default_index_mode, action=default_index_action):
"""
Identifies preclonal groups by V, J and junction length
Arguments:
db_iter : an iterator of Receptor objects defined by ChangeoReader
group_fields : additional annotation fields to use to group preclones;
if None use only V, J and junction length
mode : specificity of alignment call to use for assigning preclones;
one of ('allele', 'gene')
action : how to handle multiple value fields when assigning preclones;
one of ('first', 'set')
Returns:
dict: dictionary of {(V, J, junction length):[Receptor]}
"""
# print(fields)
# Define functions for grouping keys
if mode == 'allele' and group_fields is None:
def _get_key(rec, act):
return [rec.getVAllele(act, field=v_field), rec.getJAllele(act, field=j_field),
None if rec.junction is None else len(rec.junction)]
elif mode == 'gene' and group_fields is None:
def _get_key(rec, act):
return [rec.getVGene(act, field=v_field), rec.getJGene(act, field=j_field),
None if rec.junction is None else len(rec.junction)]
elif mode == 'allele' and group_fields is not None:
def _get_key(rec, act):
vdj = [rec.getVAllele(act, field=v_field), rec.getJAllele(act, field=j_field),
None if rec.junction is None else len(rec.junction)]
ann = [rec.getField(k) for k in group_fields]
return list(chain(vdj, ann))
elif mode == 'gene' and group_fields is not None:
def _get_key(rec, act):
vdj = [rec.getVGene(act, field=v_field), rec.getJGene(act, field=j_field),
None if rec.junction is None else len(rec.junction)]
ann = [rec.getField(k) for k in group_fields]
return list(chain(vdj, ann))
# Function to flatten nested dictionary
def _flatten_dict(d, parent_key=''):
items = []
for k, v in d.items():
new_key = parent_key + [k] if parent_key else [k]
if isinstance(v, dict):
items.extend(_flatten_dict(v, new_key).items())
else:
items.append((new_key, v))
flat_dict = {None if None in i[0] else tuple(i[0]): i[1] for i in items}
return flat_dict
if action == 'first':
index_func = indexByIdentity
elif action == 'set':
index_func = indexByUnion
else:
sys.stderr.write('Unrecognized action: %s.\n' % action)
start_time = time()
clone_index = {}
rec_count = 0
for rec in db_iter:
key = _get_key(rec, action)
# Print progress
printCount(rec_count, step=1000, start_time=start_time, task='Grouping sequences')
rec_count += 1
# Assigned passed preclone records to key and failed to index None
if all([k is not None and k != '' for k in key]):
# Update index dictionary
index_func(clone_index, key, rec, group_fields)
else:
clone_index.setdefault(None, []).append(rec)
printCount(rec_count, step=1000, start_time=start_time, task='Grouping sequences', end=True)
if action == 'set':
clone_index = _flatten_dict(clone_index)
return clone_index
def distanceClones(result, seq_field=default_junction_field, model=default_distance_model,
distance=default_distance, dist_mat=None, norm=default_norm, sym=default_sym,
linkage=default_linkage):
"""
Separates a set of Receptor objects into clones
Arguments:
result : a changeo.Multiprocessing.DbResult object with filtered records to clone
seq_field : sequence field used to calculate distance between records
model : substitution model used to calculate distance
distance : the distance threshold to assign clonal groups
dist_mat : pandas DataFrame of pairwise nucleotide or amino acid distances
norm : normalization method
sym : symmetry method
linkage : type of linkage
Returns:
changeo.Multiprocessing.DbResult : an updated DbResult object
"""
# Get distance matrix if not provided
if dist_mat is None:
try:
dist_mat = distance_models[model]
except KeyError:
printError('Unrecognized distance model: %s' % args_dict['model'])
# TODO: can be cleaned up with abstract model class
# Determine length of n-mers
if model in ['hs1f_compat', 'm1n_compat', 'aa', 'ham', 'hh_s1f', 'mk_rs1nf']:
nmer_len = 1
elif model in ['hh_s5f', 'mk_rs5nf']:
nmer_len = 5
else:
printError('Unrecognized distance model: %s.\n' % model)
# Define unique junction mapping
seq_map = {}
for rec in result.data_pass:
seq = rec.getField(seq_field)
seq = re.sub('[\.-]', 'N', seq)
if model == 'aa': seq = translate(seq)
seq_map.setdefault(seq, []).append(rec)
# Define sequences
sequences = list(seq_map.keys())
# Zero record case
if not sequences:
result.valid = False
result.log['CLONES'] = 0
return result
# Single record case
if len(sequences) == 1:
result.results = {1: result.data_pass}
result.valid = True
result.log['CLONES'] = 1
return result
# Calculate pairwise distance matrix
dists = calcDistances(sequences, nmer_len, dist_mat, sym=sym, norm=norm)
# Perform hierarchical clustering
clusters = formClusters(dists, linkage, distance)
# Turn clusters into clone dictionary
clone_dict = {}
for i, c in enumerate(clusters):
clone_dict.setdefault(c, []).extend(seq_map[sequences[i]])
if clone_dict:
result.results = clone_dict
result.valid = True
result.log['CLONES'] = len(clone_dict)
else:
result.log['CLONES'] = 0
return result
def collectQueue(alive, result_queue, collect_queue, db_file, fields,
writer=ChangeoWriter, out_file=None, out_args=default_out_args):
"""
Assembles results from a queue of individual sequence results and manages log/file I/O
Arguments:
alive = a multiprocessing.Value boolean controlling whether processing continues
if False exit process
result_queue : a multiprocessing.Queue holding processQueue results
collect_queue : a multiprocessing.Queue to store collector return values
db_file : the input database file name
fields : list of output field names
writer : writer class.
out_file : output file name. Automatically generated from the input file if None.
out_args : common output argument dictionary from parseCommonArgs
Returns:
None : Adds a dictionary with key value pairs to collect_queue containing
'log' defining a log object along with the 'pass' and 'fail' output file names.
"""
# Wrapper for opening handles and writers
def _open(x, f, writer=writer, out_file=out_file):
if out_file is not None and x == 'pass':
handle = open(out_file, 'w')
else:
handle = getOutputHandle(db_file,
out_label='clone-%s' % x,
out_dir=out_args['out_dir'],
out_name=out_args['out_name'],
out_type=out_args['out_type'])
return handle, writer(handle, fields=f)
# Open log file
try:
# Count input records
result_count = countDbFile(db_file)
# Define log handle
if out_args['log_file'] is None:
log_handle = None
else:
log_handle = open(out_args['log_file'], 'w')
except:
#sys.stderr.write('Exception in collector file opening step\n')
alive.value = False
raise
# Get results from queue and write to files
try:
# Initialize handles, writers and counters
pass_handle, pass_writer = None, None
fail_handle, fail_writer = None, None
rec_count, clone_count, pass_count, fail_count = 0, 0, 0, 0
start_time = time()
# Iterator over results queue until sentinel object reached
while alive.value:
# Get result from queue
if result_queue.empty(): continue
else: result = result_queue.get()
# Exit upon reaching sentinel
if result is None: break
# Print progress for previous iteration and update record count
printProgress(rec_count, result_count, 0.05, start_time=start_time, task='Assigning clones')
rec_count += len(result.data)
# Write passed and failed records
if result:
# Writing passing sequences
for clone in result.results.values():
clone_count += 1
for i, rec in enumerate(clone, start=1):
pass_count += 1
rec.setField('clone', str(clone_count))
result.log['CLONE%i-%i' % (clone_count, i)] = rec.junction
try:
pass_writer.writeReceptor(rec)
except AttributeError:
# Open pass file and define writer object
pass_handle, pass_writer = _open('pass', fields)
pass_writer.writeReceptor(rec)
# Write failed sequences from passing sets
if result.data_fail:
# Write failed sequences
for i, rec in enumerate(result.data_fail, start=1):
fail_count += 1
result.log['FAIL%i-%i' % (clone_count, i)] = rec.junction
if out_args['failed']:
try:
fail_writer.writeReceptor(rec)
except AttributeError:
# Open fail file and define writer object
fail_handle, fail_writer = _open('fail', fields)
fail_writer.writeReceptor(rec)
else:
# Write failing records
for i, rec in enumerate(result.data, start=1):
fail_count += 1
result.log['CLONE0-%i' % (i)] = rec.junction
if out_args['failed']:
try:
fail_writer.writeReceptor(rec)
except AttributeError:
# Open fail file and define writer object
fail_handle, fail_writer = _open('fail', fields)
fail_writer.writeReceptor(rec)
# Write log
printLog(result.log, handle=log_handle)
else:
sys.stderr.write('PID %s> Error in sibling process detected. Cleaning up.\n' \
% os.getpid())
return None
# Print total counts
printProgress(rec_count, result_count, 0.05, start_time=start_time, task='Assigning clones')
# Update return list
log = OrderedDict()
log['OUTPUT'] = os.path.basename(pass_handle.name) if pass_handle is not None else None
log['CLONES'] = clone_count
log['RECORDS'] = rec_count
log['PASS'] = pass_count
log['FAIL'] = fail_count
# Close file handles and generate return data
collect_dict = {'log': log, 'pass': None, 'fail': None}
if pass_handle is not None:
collect_dict['pass'] = pass_handle.name
pass_handle.close()
if fail_handle is not None:
collect_dict['fail'] = fail_handle.name
fail_handle.close()
if log_handle is not None:
log_handle.close()
collect_queue.put(collect_dict)
except:
alive.value = False
raise
return None
def defineClones(db_file, seq_field=default_junction_field, v_field=default_v_field,
j_field=default_j_field, max_missing=default_max_missing,
group_fields=None, group_func=groupByGene, group_args={},
clone_func=distanceClones, clone_args={},
format=default_format, out_file=None, out_args=default_out_args,
nproc=None, queue_size=None):
"""
Define clonally related sequences
Arguments:
db_file : filename of input database.
seq_field : sequence field used to determine clones.
v_field : field containing the V call.
j_field : field containing the J call.
max_missing : maximum number of non-ACGT characters to allow in the junction sequence.
group_fields : additional annotation fields to use to group preclones;
if None use only V and J.
group_func : the function to use for assigning preclones.
group_args : a dictionary of arguments to pass to group_func.
clone_func : the function to use for determining clones within preclonal groups.
clone_args : a dictionary of arguments to pass to clone_func.
format : input and output format.
out_file : output file name. Automatically generated from the input file if None.
out_args : common output argument dictionary from parseCommonArgs.
nproc : the number of processQueue processes;
if None defaults to the number of CPUs.
queue_size : maximum size of the argument queue;
if None defaults to 2*nproc.
Returns:
dict: dictionary of output pass and fail files.
"""
# Print parameter info
log = OrderedDict()
log['START'] = 'DefineClones'
log['FILE'] = os.path.basename(db_file)
log['SEQ_FIELD'] = seq_field
log['V_FIELD'] = v_field
log['J_FIELD'] = j_field
log['MAX_MISSING'] = max_missing
log['GROUP_FIELDS'] = ','.join(group_fields) if group_fields is not None else None
for k in sorted(group_args):
log[k.upper()] = group_args[k]
for k in sorted(clone_args):
if k != 'dist_mat': log[k.upper()] = clone_args[k]
log['NPROC'] = nproc
printLog(log)
# Define format operators
try:
reader, writer, schema = getFormatOperators(format)
except ValueError:
printError('Invalid format %s.' % format)
# Translate to Receptor attribute names
seq_field = schema.toReceptor(seq_field)
v_field = schema.toReceptor(v_field)
j_field = schema.toReceptor(j_field)
if group_fields is not None:
group_fields = [schema.toReceptor(f) for f in group_fields]
# Define feeder function and arguments
group_args['group_fields'] = group_fields
group_args['v_field'] = v_field
group_args['j_field'] = j_field
feed_args = {'db_file': db_file,
'reader': reader,
'group_func': group_func,
'group_args': group_args}
# Define worker function and arguments
filter_args = {'seq_field': seq_field,
'v_field': v_field,
'j_field': j_field,
'max_missing': max_missing}
clone_args['seq_field'] = seq_field
work_args = {'process_func': clone_func,
'process_args': clone_args,
'filter_func': filterMissing,
'filter_args': filter_args}
# Define collector function and arguments
out_fields = getDbFields(db_file, add=schema.fromReceptor('clone'), reader=reader)
out_args['out_type'] = schema.out_type
collect_args = {'db_file': db_file,
'fields': out_fields,
'writer': writer,
'out_file': out_file,
'out_args': out_args}
# Check for required columns
try:
required = ['junction']
checkFields(required, out_fields, schema=schema)
except LookupError as e:
printError(e)
# Call process manager
result = manageProcesses(feed_func=feedDbQueue, work_func=processDbQueue, collect_func=collectQueue,
feed_args=feed_args, work_args=work_args, collect_args=collect_args,
nproc=nproc, queue_size=queue_size)
# Print log
result['log']['END'] = 'DefineClones'
printLog(result['log'])
output = {k: v for k, v in result.items() if k in ('pass', 'fail')}
return output
def getArgParser():
"""
Defines the ArgumentParser
Arguments:
None
Returns:
an ArgumentParser object
"""
# Define input and output fields
fields = dedent(
'''
output files:
clone-pass
database with assigned clonal group numbers.
clone-fail
database with records failing clonal grouping.
required fields:
SEQUENCE_ID, V_CALL, J_CALL, JUNCTION
output fields:
CLONE
''')
# Define argument parser
parser = ArgumentParser(description=__doc__, epilog=fields,
parents=[getCommonArgParser(format=False, multiproc=True)],
formatter_class=CommonHelpFormatter, add_help=False)
# Distance cloning method
group = parser.add_argument_group('cloning arguments')
group.add_argument('--sf', action='store', dest='seq_field', default=default_junction_field,
help='Field to be used to calculate distance between records.')
group.add_argument('--vf', action='store', dest='v_field', default=default_v_field,
help='Field containing the germline V segment call.')
group.add_argument('--jf', action='store', dest='j_field', default=default_j_field,
help='Field containing the germline J segment call.')
group.add_argument('--gf', nargs='+', action='store', dest='group_fields', default=None,
help='Additional fields to use for grouping clones aside from V, J and junction length.')
group.add_argument('--mode', action='store', dest='mode',
choices=('allele', 'gene'), default=default_index_mode,
help='''Specifies whether to use the V(D)J allele or gene for
initial grouping.''')
group.add_argument('--act', action='store', dest='action',
choices=('first', 'set'), default=default_index_action,
help='''Specifies how to handle multiple V(D)J assignments for initial grouping.
The "first" action will use only the first gene listed.
The "set" action will use all gene assignments and construct a larger gene
grouping composed of any sequences sharing an assignment or linked to another
sequence by a common assignment (similar to single-linkage).''')
group.add_argument('--model', action='store', dest='model',
choices=choices_distance_model,
default=default_distance_model,
help='''Specifies which substitution model to use for calculating distance
between sequences. The "ham" model is nucleotide Hamming distance and
"aa" is amino acid Hamming distance. The "hh_s1f" and "hh_s5f" models are
human specific single nucleotide and 5-mer content models, respectively,
from Yaari et al, 2013. The "mk_rs1nf" and "mk_rs5nf" models are
mouse specific single nucleotide and 5-mer content models, respectively,
from Cui et al, 2016. The "m1n_compat" and "hs1f_compat" models are
deprecated models provided backwards compatibility with the "m1n" and
"hs1f" models in Change-O v0.3.3 and SHazaM v0.1.4. Both
5-mer models should be considered experimental.''')
group.add_argument('--dist', action='store', dest='distance', type=float,
default=default_distance,
help='The distance threshold for clonal grouping')
group.add_argument('--norm', action='store', dest='norm',
choices=('len', 'mut', 'none'), default=default_norm,
help='''Specifies how to normalize distances. One of none
(do not normalize), len (normalize by length),
or mut (normalize by number of mutations between sequences).''')
group.add_argument('--sym', action='store', dest='sym',
choices=('avg', 'min'), default=default_sym,
help='''Specifies how to combine asymmetric distances. One of avg
(average of A->B and B->A) or min (minimum of A->B and B->A).''')
group.add_argument('--link', action='store', dest='linkage',
choices=('single', 'average', 'complete'), default=default_linkage,
help='''Type of linkage to use for hierarchical clustering.''')
group.add_argument('--maxmiss', action='store', dest='max_missing', type=int,
default=default_max_missing,
help='''The maximum number of non-ACGT characters (gaps or Ns) to
permit in the junction sequence before excluding the record
from clonal assignment. Note, under single linkage
non-informative positions can create artifactual links
between unrelated sequences. Use with caution.''')
parser.set_defaults(group_func=groupByGene)
parser.set_defaults(clone_func=distanceClones)
return parser
if __name__ == '__main__':
"""
Parses command line arguments and calls main function
"""
# Parse arguments
parser = getArgParser()
checkArgs(parser)
args = parser.parse_args()
args_dict = parseCommonArgs(args)
# # Set default fields if not specified.
# default_fields = {'seq_field': default_junction_field,
# 'v_field': default_v_field,
# 'j_field': default_j_field}
#
# # Default Change-O fields
# if args_dict['format'] == 'changeo':
# for f in default_fields:
# if args_dict[f] is None: args_dict[f] = default_fields[f]
# else: args_dict[f] = args_dict[f].upper()
#
# # Default AIRR fields
# if args_dict['format'] == 'airr':
# for f in default_fields:
# if args_dict[f] is None: args_dict[f] = ChangeoSchema.toAIRR(default_fields[f])
# else: args_dict[f] = args_dict[f].lower()
# Define grouping and cloning function arguments
args_dict['group_args'] = {'action': args_dict['action'],
'mode':args_dict['mode']}
args_dict['clone_args'] = {'model': args_dict['model'],
'distance': args_dict['distance'],
'norm': args_dict['norm'],
'sym': args_dict['sym'],
'linkage': args_dict['linkage']}
# Get distance matrix
try:
args_dict['clone_args']['dist_mat'] = distance_models[args_dict['model']]
except KeyError:
printError('Unrecognized distance model: %s' % args_dict['model'])
# Clean argument dictionary
del args_dict['action']
del args_dict['mode']
del args_dict['model']
del args_dict['distance']
del args_dict['norm']
del args_dict['sym']
del args_dict['linkage']
# Clean arguments dictionary
del args_dict['db_files']
if 'out_files' in args_dict: del args_dict['out_files']
# Call main function for each input file
for i, f in enumerate(args.__dict__['db_files']):
args_dict['db_file'] = f
args_dict['out_file'] = args.__dict__['out_files'][i] \
if args.__dict__['out_files'] else None
defineClones(**args_dict)
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