def match(args):
"""
Matches all trials in database to patients
:param daemon: Boolean flag; when true, runs the matchengine once per 24 hours.
"""
db = get_db(args.mongo_uri)
while True:
me = MatchEngine(db)
me.find_trial_matches()
# exit if it is not set to run as a nightly automated daemon, otherwise sleep for a day
if not args.daemon:
# choose output file format
if args.json_format:
file_format = 'json'
elif args.outpath and len(args.outpath.split('.')) > 1:
file_format = args.outpath.split('.')[-1]
if file_format not in ['json', 'csv']:
file_format = 'csv'
else:
file_format = 'csv'
# choose output path
if args.outpath:
outpath = args.outpath.split('.')[0]
else:
outpath = './results'
# export results
export_results(args.mongo_uri, file_format, outpath)
break
else:
time.sleep(86400) # sleep for 24 hours
def setUp(self):
"""
Descriptions of test patients
1: >18, Adrenal Gland, Female, BRAF F346R Mutation
2: >18, Melanoma, Female, EGFR L858R Mutation
3: >18, Melanoma, Female, EGFR F346A Mutation
4: >18, Melanoma, Female, EGFR F346B Mutation
5: >18, Melanoma, Female, EGFR F000F Mutation
6: >0.5 && <18, Melanoma, Male, EGFR SV
7: >0.5 && <18, Glioblastoma, Male, EGFR CNV Hetero del
8: >0.5 && <18, Glioblastoma, Male, EGFR CNV Gain
9: >0.5 && <18, Glioblastoma, Male, EGFR CNV H**o del
10: <0.5, Glioblastoma, Male, EGFR CNV High amp
Descriptions of test trials
00-001.yml: dose: EGFR L858R && >=18/_SOLID_
00-002.yml: arm: EGFR L858R && >=18/_SOLID_
00-003.yml: step: EGFR L858R && >=18/_SOLID_
00-004.yml dose: EGFR L858R && >=18/_SOLID_
00-005.yml 2 doses: EGFR L858R && >=18/_SOLID_
00-006.yml exon: !13
"""
self.db = get_db(None)
for res in ["clinical", "dashboard", "filter", "genomic", "hipaa", "match", "normalize", "oplog"
"response", "statistics", "status", "team", "trial", "trial_match", "user"]:
self.db.drop_collection(res)
self.me = MatchEngine(self.db)
self.trials = {}
self.clinical_id = ObjectId()
self.mrn = 'TCGA-BH-A1FR'
self.sample_id = 'TCGA-OR-A5J1'
self.mrns = [self.mrn] + [self.__random_id() for _ in range(9)]
self.sample_ids = [self.sample_id] + [self.__random_id() for _ in range(9)]
self.clinical_ids = [self.clinical_id] + [ObjectId() for _ in range(9)]
self.static_date = dt.datetime.today()
# clinical collection
self.oncotree_diagnoses = ['Adrenal Gland'] + ['Melanoma'] * 5 + ['Glioblastoma'] * 4
self.genders = ['Female'] * 5 + ['Male'] * 5
# ages
adult = self.static_date - dt.timedelta(days=365*19)
child = self.static_date - dt.timedelta(days=365*10)
infant = self.static_date - dt.timedelta(days=30*4)
self.ages = [adult] * 5 + [child] * 4 + [infant]
self.clinical = [{
'_id': clinical_id,
'ONCOTREE_PRIMARY_DIAGNOSIS_NAME': diagnosis,
'SAMPLE_ID': sample_id,
'VITAL_STATUS': 'alive',
'MRN': mrn,
'REPORT_DATE': self.static_date,
'BIRTH_DATE': age,
'GENDER': gender
} for diagnosis, gender, age, clinical_id, sample_id, mrn in zip(
self.oncotree_diagnoses, self.genders, self.ages, self.clinical_ids, self.sample_ids, self.mrns)]
# genomic collection
self.genes = ['BRAF'] + ['EGFR'] * 9
self.protein_changes = ['p.F346R', 'p.L858R', 'p.F346A', 'p.F346B', 'p.F000F', None, None, None, None, None]
self.variant_categories = ['MUTATION'] * 5 + ['SV', 'CNV', 'CNV', 'CNV', 'CNV']
self.wildtypes = [False] * 10
self.cnv_calls = [None, None, None, None, None, None,
'Heterozygous deletion', 'Gain', 'Homozygous deletion', 'High level amplification']
self.genomic = [{
'TRUE_VARIANT_CLASSIFICATION': 'In_Frame_Del',
'TRUE_PROTEIN_CHANGE': protein_change,
'VARIANT_CATEGORY': variant_category,
'CHROMOSOME': 'chr3',
'POSITION': 178952085,
'TRUE_STRAND': '+',
'WILDTYPE': wildtype,
'CLINICAL_ID': _id,
'CNV_CALL': cnv_call,
'TRUE_HUGO_SYMBOL': gene,
'SAMPLE_ID': sample_id,
'TRUE_TRANSCRIPT_EXON': 19
} for protein_change, variant_category, wildtype, cnv_call, gene, _id, sample_id in zip(
self.protein_changes, self.variant_categories, self.wildtypes,
self.cnv_calls, self.genes, self.clinical_ids, self.sample_ids
)]
# test trials
self.test_trials = ['00-001', '00-002', '00-003']
# demo match results
pnos = ['00-001', '00-001', '00-001', '00-002', '00-002', '00-002']
mlevels = ['arm', 'arm', 'arm', 'dose', 'dose', 'dose']
iids = ['1', '2', '3', '4', '5', '6']
galts = ['Alt1', 'Alt2', 'Alt2', 'Alt3', 'Alt3', 'Alt3']
self.matches = [{
'mrn': 'SAMPLE1',
'sample_id': 'SAMPLE1-ID',
'protocol_no': protocol_no,
'match_level': match_level,
'internal_id': internal_id,
'genomic_alteration': genomic_alteration
} for protocol_no, match_level, internal_id, genomic_alteration in zip(
pnos, mlevels, iids, galts
)]
def load(args):
"""
Sets up MongoDB for matching
:param args: clinical: Path to csv file containing clinical data. Required fields are:
- MRN (Unique patient identifier)
- SAMPLE_ID (Unique sample identifier)
- ONCOTREE_PRIMARY_DIAGNOSIS_NAME (Disease diagnosis)
- BIRTH_DATE (Date of birth in format 'YYYY-MM-DD 00:00:00.000')
Suggested additional fields:
- ORD_PHYSICIAN_NAME
- ORD_PHYSICIAN_EMAIL
- REPORT_DATE
- VITAL_STATUS (alive or deceased)
- FIRST_LAST (Patient's first and last name)
- GENDER (Male or Female)
:param args: genomic: Path to csv file containing genomic data. The following fields are used in matching:
- SAMPLE_ID (Unique sample identifier)
- TRUE_HUGO_SYMBOL (Gene name)
- TRUE_PROTEIN_CHANGE (Specific variant)
- TRUE_VARIANT_CLASSIFICATION (Variant type)
- VARIANT_CATEGORY (CNV, MUTATION, or SV)
- TRUE_TRANSCRIPT_EXON (Exon number <integer>
- CNV_CALL (Heterozygous deletion, Homozygous deletion, Gain, High Level amplification, or null)
- WILDTYPE (True or False)
Suggested additional fields:
- CHROMOSOME (Chromosome number in format 'chr01')
- POSITION <integer>
- TRUE_CDNA_CHANGE
- REFERENCE_ALLELE
- CANONICAL_STRAND (- or +)
- ALLELE_FRACTION <float>
- TIER <integer>
:param args: trials: Path to bson trial file.
"""
db = get_db(args.mongo_uri)
t = Trial(db)
p = Patient(db)
# Add trials to mongo
if args.trials:
logging.info('Adding trials to mongo...')
t.load_dict[args.trial_format](args.trials)
# Add patient data to mongo
if args.clinical and args.genomic:
logging.info('Reading data into pandas...')
is_bson = p.load_dict[args.patient_format](args.clinical, args.genomic)
if not is_bson:
# reformatting
for col in ['BIRTH_DATE', 'REPORT_DATE']:
try:
p.clinical_df[col] = p.clinical_df[col].apply(lambda x: str(dt.datetime.strptime(x, '%Y-%m-%d')))
except ValueError as exc:
if col == 'BIRTH_DATE':
print '## WARNING ## Birth dates should be formatted %Y-%m-%d to be properly stored in MongoDB.'
print '## ## Birth dates may be malformed in the database and will therefore not match'
print '## ## trial age restrictions properly.'
print '## ## System error: \n%s' % exc
p.genomic_df['TRUE_TRANSCRIPT_EXON'] = p.genomic_df['TRUE_TRANSCRIPT_EXON'].apply(
lambda x: int(x) if x != '' and pd.notnull(x) else x)
# Add clinical data to mongo
logging.info('Adding clinical data to mongo...')
clinical_json = json.loads(p.clinical_df.T.to_json()).values()
for item in clinical_json:
for col in ['BIRTH_DATE', 'REPORT_DATE']:
if col in item:
item[col] = dt.datetime.strptime(str(item[col]), '%Y-%m-%d %X')
db.clinical.insert(clinical_json)
# Get clinical ids from mongo
logging.info('Adding clinical ids to genomic data...')
clinical_doc = list(db.clinical.find({}, {"_id": 1, "SAMPLE_ID": 1}))
clinical_dict = dict(zip([i['SAMPLE_ID'] for i in clinical_doc], [i['_id'] for i in clinical_doc]))
# pd -> json
if args.trial_format == 'pkl':
genomic_json = json.loads(p.genomic_df.to_json(orient='records'))
else:
genomic_json = json.loads(p.genomic_df.T.to_json()).values()
# Map clinical ids to genomic data
for item in genomic_json:
if item['SAMPLE_ID'] in clinical_dict:
item["CLINICAL_ID"] = clinical_dict[item['SAMPLE_ID']]
else:
item["CLINICAL_ID"] = None
# Add genomic data to mongo
logging.info('Adding genomic data to mongo...')
db.genomic.insert(genomic_json)
# Create index
logging.info('Creating index...')
db.genomic.create_index([("TRUE_HUGO_SYMBOL", ASCENDING), ("WILDTYPE", ASCENDING)])
elif args.clinical and not args.genomic or args.genomic and not args.clinical:
logging.error('If loading patient information, please provide both clinical and genomic data.')
sys.exit(1)
