def run_pipeline(patient_id, score_epitopes):
"""Run the pipeline for this patient, and save the output to the DB as a
Run."""
hla_types = HLAType.query.with_entities(HLAType.allele,
HLAType.mhc_class).filter_by(patient_id=patient_id).all()
peptide_length = 31
alleles = [normalize_hla_allele_name(
allele) for allele, mhc_class in hla_types]
vcf_df = get_vcf_df(patient_id)
transcripts_df, vcf_df, variant_report = expand_transcripts(
vcf_df,
patient_id,
min_peptide_length = peptide_length,
max_peptide_length = peptide_length)
scored_epitopes = score_epitopes(transcripts_df, alleles)
imm = ImmunogenicityPredictor(alleles=alleles)
scored_epitopes = imm.predict(scored_epitopes)
# TODO(tavi) Make this expansion more robust. It breaks the IEDB predictor,
# for example.
short_transcripts_df = transcripts_df[['chr', 'pos', 'ref',
'alt', 'TranscriptId']]
scored_epitopes = merge(scored_epitopes, short_transcripts_df,
on='TranscriptId', how='left')
peptides = group_epitopes_dataframe(
scored_epitopes, use_transcript_name = True)
run = Run(patient_id=patient_id, output=dumps(peptides))
db.session.add(run)
def generate_mutation_counts(
mutation_files,
hla_types,
genes_expressed,
max_peptide_length=31,
skip_identifiers = {},
output_file=None):
"""
Returns dictionary that maps each patient ID to a tuple with six fields:
- total number of mutated epitopes across all transcripts
- number of mutated genes
- number of mutated genes with MHC binding mutated epitope
- number of mutated epitopes which are predicted to bind to an MHC
allele
- number of mutated genes with at least one immunogenic mutated
epitope
- number of mutated epitopes which are predicted to be immunogenic
(MHC binder + non-self)
"""
mutation_counts = OrderedDict()
n = len(mutation_files)
for i, (patient_id, vcf_df) in enumerate(mutation_files.iteritems()):
if patient_id in skip_identifiers:
logging.info("Skipping patient ID %s", patient_id)
continue
hla_allele_names = hla_types[patient_id]
logging.info(
"Processing %s (#%d/%d) with HLA alleles %s",
patient_id, i + 1, n, hla_allele_names)
if not args.quiet:
print vcf_df
try:
transcripts_df, raw_genomic_mutation_df, variant_report = (
expand_transcripts(
vcf_df,
patient_id,
max_peptide_length=max_peptide_length))
except KeyboardInterrupt:
raise
except:
logging.warning("Failed to apply mutations for %s", patient_id)
raise
# print each genetic mutation applied to each possible transcript
# and either why it failed or what protein mutation resulted
if not args.quiet:
print_mutation_report(
patient_id,
variant_report,
raw_genomic_mutation_df,
transcripts_df)
logging.info(
"Calling MHC binding predictor for %s (#%d/%d)",
patient_id, i + 1, n)
def make_mhc_predictor():
if args.netmhc_cons:
return ConsensusBindingPredictor(hla_allele_names)
else:
return PanBindingPredictor(hla_allele_names)
# If we want to read scored_epitopes from a CSV file, do that.
if args.debug_scored_epitopes_csv:
csv_file = args.debug_scored_epitopes_csv
if isfile(csv_file):
scored_epitopes = pd.read_csv(csv_file)
else:
mhc = make_mhc_predictor()
scored_epitopes = mhc.predict(transcripts_df,
mutation_window_size=9)
scored_epitopes.to_csv(csv_file)
else:
mhc = make_mhc_predictor()
scored_epitopes = mhc.predict(transcripts_df,
mutation_window_size=9)
if not args.quiet:
print scored_epitopes
imm = ImmunogenicityPredictor(
alleles=hla_allele_names,
binding_threshold=args.binding_threshold)
scored_epitopes = imm.predict(scored_epitopes)
scored_epitopes.to_csv("scored_epitopes.csv")
scored_epitopes = pd.read_csv("scored_epitopes.csv")
grouped = scored_epitopes.groupby(["Gene", "GeneMutationInfo"])
n_coding_mutations = len(grouped)
n_epitopes = 0
n_ligand_mutations = 0
n_ligands = 0
n_immunogenic_mutations = 0
n_immunogenic_epitopes = 0
for (gene, mut), group in grouped:
start_mask = group.EpitopeStart < group.MutationEnd
stop_mask = group.EpitopeEnd > group.MutationStart
mutated_subset = group[start_mask & stop_mask]
# we might have duplicate epitopes from multiple transcripts, so
# drop them
n_curr_epitopes = len(mutated_subset.groupby(['Epitope']))
n_epitopes += n_curr_epitopes
below_threshold_mask = \
mutated_subset.MHC_IC50 <= args.binding_threshold
ligands = mutated_subset[below_threshold_mask]
n_curr_ligands = len(ligands.groupby(['Epitope']))
n_ligands += n_curr_ligands
n_ligand_mutations += (n_curr_ligands) > 0
thymic_deletion_mask = \
np.array(ligands.ThymicDeletion).astype(bool)
immunogenic_epitopes = ligands[~thymic_deletion_mask]
curr_immunogenic_epitopes = immunogenic_epitopes.groupby(['Epitope']).first()
n_immunogenic_epitopes += len(curr_immunogenic_epitopes)
n_immunogenic_mutations += len(curr_immunogenic_epitopes) > 0
logging.info(("%s %s: epitopes %s, ligands %d, imm %d"),
gene,
mut,
n_curr_epitopes,
n_curr_ligands,
len(curr_immunogenic_epitopes),
)
result_tuple = (
n_coding_mutations,
n_epitopes,
n_ligand_mutations,
n_ligands,
n_immunogenic_mutations,
n_immunogenic_epitopes,
)
if output_file:
data_string = ",".join(str(d) for d in result_tuple)
output_file.write("%s,%s\n" % (patient_id, data_string))
output_file.flush()
mutation_counts[patient_id] = result_tuple
return mutation_counts
def generate_mutation_counts(mutation_files,
hla_types,
genes_expressed,
max_peptide_length=31,
skip_identifiers={},
output_file=None):
"""
Returns dictionary that maps each patient ID to a tuple with six fields:
- total number of mutated epitopes across all transcripts
- number of mutated genes
- number of mutated genes with MHC binding mutated epitope
- number of mutated epitopes which are predicted to bind to an MHC
allele
- number of mutated genes with at least one immunogenic mutated
epitope
- number of mutated epitopes which are predicted to be immunogenic
(MHC binder + non-self)
"""
mutation_counts = OrderedDict()
n = len(mutation_files)
for i, (patient_id, vcf_df) in enumerate(mutation_files.iteritems()):
if patient_id in skip_identifiers:
logging.info("Skipping patient ID %s", patient_id)
continue
hla_allele_names = hla_types[patient_id]
logging.info("Processing %s (#%d/%d) with HLA alleles %s", patient_id,
i + 1, n, hla_allele_names)
if not args.quiet:
print vcf_df
try:
transcripts_df, raw_genomic_mutation_df, variant_report = (
expand_transcripts(vcf_df,
patient_id,
max_peptide_length=max_peptide_length))
except KeyboardInterrupt:
raise
except:
logging.warning("Failed to apply mutations for %s", patient_id)
raise
# print each genetic mutation applied to each possible transcript
# and either why it failed or what protein mutation resulted
if not args.quiet:
print_mutation_report(patient_id, variant_report,
raw_genomic_mutation_df, transcripts_df)
logging.info("Calling MHC binding predictor for %s (#%d/%d)",
patient_id, i + 1, n)
def make_mhc_predictor():
if args.netmhc_cons:
return ConsensusBindingPredictor(hla_allele_names)
else:
return PanBindingPredictor(hla_allele_names)
# If we want to read scored_epitopes from a CSV file, do that.
if args.debug_scored_epitopes_csv:
csv_file = args.debug_scored_epitopes_csv
if isfile(csv_file):
scored_epitopes = pd.read_csv(csv_file)
else:
mhc = make_mhc_predictor()
scored_epitopes = mhc.predict(transcripts_df,
mutation_window_size=9)
scored_epitopes.to_csv(csv_file)
else:
mhc = make_mhc_predictor()
scored_epitopes = mhc.predict(transcripts_df,
mutation_window_size=9)
if not args.quiet:
print scored_epitopes
imm = ImmunogenicityPredictor(alleles=hla_allele_names,
binding_threshold=args.binding_threshold)
scored_epitopes = imm.predict(scored_epitopes)
scored_epitopes.to_csv("scored_epitopes.csv")
scored_epitopes = pd.read_csv("scored_epitopes.csv")
grouped = scored_epitopes.groupby(["Gene", "GeneMutationInfo"])
n_coding_mutations = len(grouped)
n_epitopes = 0
n_ligand_mutations = 0
n_ligands = 0
n_immunogenic_mutations = 0
n_immunogenic_epitopes = 0
for (gene, mut), group in grouped:
start_mask = group.EpitopeStart < group.MutationEnd
stop_mask = group.EpitopeEnd > group.MutationStart
mutated_subset = group[start_mask & stop_mask]
# we might have duplicate epitopes from multiple transcripts, so
# drop them
n_curr_epitopes = len(mutated_subset.groupby(['Epitope']))
n_epitopes += n_curr_epitopes
below_threshold_mask = \
mutated_subset.MHC_IC50 <= args.binding_threshold
ligands = mutated_subset[below_threshold_mask]
n_curr_ligands = len(ligands.groupby(['Epitope']))
n_ligands += n_curr_ligands
n_ligand_mutations += (n_curr_ligands) > 0
thymic_deletion_mask = \
np.array(ligands.ThymicDeletion).astype(bool)
immunogenic_epitopes = ligands[~thymic_deletion_mask]
curr_immunogenic_epitopes = immunogenic_epitopes.groupby(
['Epitope']).first()
n_immunogenic_epitopes += len(curr_immunogenic_epitopes)
n_immunogenic_mutations += len(curr_immunogenic_epitopes) > 0
logging.info(
("%s %s: epitopes %s, ligands %d, imm %d"),
gene,
mut,
n_curr_epitopes,
n_curr_ligands,
len(curr_immunogenic_epitopes),
)
result_tuple = (
n_coding_mutations,
n_epitopes,
n_ligand_mutations,
n_ligands,
n_immunogenic_mutations,
n_immunogenic_epitopes,
)
if output_file:
data_string = ",".join(str(d) for d in result_tuple)
output_file.write("%s,%s\n" % (patient_id, data_string))
output_file.flush()
mutation_counts[patient_id] = result_tuple
return mutation_counts
