def encode_allele_datasets(
allele_datasets,
max_ic50,
binary_encoding=False):
"""
Parameters
----------
allele_dataset : AlleleDataset
Named tuple with fields "X" and "ic50"
max_ic50 : float
Largest IC50 value predictor should return
binary_encoding : bool (default = False)
If True, use a binary 1-of-k encoding of amino acids, otherwise
expect a vector embedding to use integer indices.
Returns three dictionarys
- mapping from allele name to X (features)
- mapping from allele name to Y_log_ic50 (continuous outputs)
- mapping from allele name to binder_label (binary outputs)
"""
X_dict = OrderedDict()
Y_log_ic50_dict = OrderedDict([])
ic50_dict = OrderedDict([])
for (allele_name, dataset) in allele_datasets.items():
allele_name = normalize_allele_name(allele_name)
(X, Y_log_ic50, Y_ic50) = encode_allele_dataset(
dataset,
max_ic50=max_ic50,
binary_encoding=binary_encoding)
X_dict[allele_name] = X
Y_log_ic50_dict[allele_name] = Y_log_ic50
ic50_dict[allele_name] = Y_ic50
return (X_dict, Y_log_ic50_dict, ic50_dict)
def run():
args = parser.parse_args(sys.argv[1:])
print(args)
total = 0
order = []
name_to_record = {}
for fasta in args.fastas:
reader = Bio.SeqIO.parse(fasta, "fasta")
for record in reader:
total += 1
if len(record.seq) < 50:
print("-- Skipping '%s', sequence too short" % (
record.description,))
continue
parts = record.description.split()
candidate_strings = [
record.description,
parts[1],
" ".join(parts[1:])
]
name = None
for candidate_string in candidate_strings:
name = normalize_allele_name(
candidate_string, raise_on_error=False)
if name is not None:
break
if name is None:
print("Skipping '%s'" % (record.description,))
continue
print("Parsed '%s' as %s" % (record.description, name))
record.description = name + " " + record.description
if name in name_to_record:
old_record = name_to_record[name]
old_sequence = old_record.seq
if len(old_sequence) < len(record.seq):
print("-- Replacing old record (%d aa) with new (%d aa)" % (
len(old_record.seq),
len(record.seq)))
name_to_record[name] = record
else:
print("-- Skipping, already seen")
else:
order.append(name)
name_to_record[name] = record
records = [name_to_record[name] for name in order]
with open(args.out, "w") as fd:
Bio.SeqIO.write(records, fd, "fasta")
print("Wrote %d / %d sequences: %s" % (len(records), total, args.out))
def filter_alleles(allele_datasets, min_samples_per_allele=5):
for (allele_name, dataset) in sorted(
allele_datasets.items(), key=lambda pair: pair[0]):
# Want alleles to be 4-digit + gene name e.g. C0401
if allele_name.isdigit() or len(allele_name) < 5:
print("Skipping allele %s" % (allele_name,))
continue
allele_name = normalize_allele_name(allele_name)
ic50_allele = dataset.ic50
n_samples_allele = len(ic50_allele)
if n_samples_allele < min_samples_per_allele:
print("Skipping allele %s due to too few samples: %d" % (
allele_name, n_samples_allele))
continue
binders = ic50_allele <= 500
if binders.all():
print("No negative examples for %s" % allele_name)
continue
if not binders.any():
print("No positive examples for %s" % allele_name)
continue
yield (allele_name, dataset)
def normalize_allele_name_or_return_unknown(s):
if s is numpy.nan:
return "UNKNOWN"
return normalize_allele_name(s,
raise_on_error=False,
default_value="UNKNOWN")
def run(argv=sys.argv[1:]):
global GLOBAL_DATA
# On sigusr1 print stack trace
print("To show stack trace, run:\nkill -s USR1 %d" % os.getpid())
signal.signal(signal.SIGUSR1, lambda sig, frame: traceback.print_stack())
args = parser.parse_args(argv)
configure_logging()
serial_run = not args.cluster_parallelism and args.num_jobs == 0
alleles = [
normalize_allele_name(a, raise_on_error=False) for a in args.allele
]
n_bad_alleles = sum([a is None for a in alleles])
if n_bad_alleles > 0:
print("Dropping %d bad alleles" % n_bad_alleles)
alleles = numpy.array(sorted({a for a in alleles if a}))
peptides = pandas.read_csv(
args.input_peptides,
nrows=args.max_peptides).peptide.drop_duplicates()
print("Filtering to valid peptides. Starting at: ", len(peptides))
peptides = peptides[peptides.str.match("^[ACDEFGHIKLMNPQRSTVWY]+$")]
print("Filtered to: ", len(peptides))
peptides = peptides.unique()
num_peptides = len(peptides)
print("Predictions for %d alleles x %d peptides." %
(len(alleles), num_peptides))
if not os.path.exists(args.out):
print("Creating", args.out)
os.mkdir(args.out)
GLOBAL_DATA["predictor"] = args.predictor
GLOBAL_DATA["args"] = args
GLOBAL_DATA["cols"] = PREDICTOR_TO_COLS[args.predictor]
# Write peptide and allele lists to out dir.
out_peptides = os.path.abspath(os.path.join(args.out, "peptides.csv"))
pandas.DataFrame({"peptide": peptides}).to_csv(out_peptides, index=False)
print("Wrote: ", out_peptides)
manifest_df = []
for allele in alleles:
for col in PREDICTOR_TO_COLS[args.predictor]:
manifest_df.append((allele, col))
manifest_df = pandas.DataFrame(manifest_df, columns=["allele", "kind"])
manifest_df["col"] = (manifest_df.allele + " " + manifest_df.kind)
manifest_df["path"] = manifest_df.col.map(
lambda s: s.replace("*", "").replace(" ", ".")) + ".npz"
out_manifest = os.path.abspath(os.path.join(args.out, "alleles.csv"))
manifest_df.to_csv(out_manifest, index=False)
col_to_filename = manifest_df.set_index("col").path.map(
lambda s: os.path.abspath(os.path.join(args.out, s)))
print("Wrote: ", out_manifest)
result_df = pandas.DataFrame(index=peptides,
columns=manifest_df.col.values,
dtype=args.result_dtype)
result_df[:] = numpy.nan
if args.reuse_predictions:
# Allocating this here to hit any memory errors as early as possible.
is_null_matrix = numpy.ones(shape=(result_df.shape[0], len(alleles)),
dtype="int8")
for dirname in args.reuse_predictions:
if not dirname:
continue # ignore empty strings
if os.path.exists(dirname):
print("Loading predictions", dirname)
result_df = load_results(dirname,
result_df,
dtype=args.result_dtype)
else:
print("WARNING: skipping because does not exist", dirname)
# We rerun any alleles that have nulls for any kind of values
# (e.g. affinity, percentile rank, elution score).
for (i, allele) in enumerate(alleles):
sub_df = manifest_df.loc[manifest_df.allele == allele]
is_null_matrix[:, i] = result_df[sub_df.col.values].isnull().any(1)
print("Fraction null", is_null_matrix.mean())
print("Grouping peptides by alleles")
allele_indices_to_peptides = collections.defaultdict(list)
for (i, peptide) in tqdm.tqdm(enumerate(peptides),
total=len(peptides)):
(allele_indices, ) = numpy.where(is_null_matrix[i])
if len(allele_indices) > 0:
allele_indices_to_peptides[tuple(allele_indices)].append(
peptide)
del is_null_matrix
work_items = []
print("Assigning peptides to work items.")
for (indices, block_peptides) in allele_indices_to_peptides.items():
num_chunks = int(math.ceil(len(block_peptides) / args.chunk_size))
peptide_chunks = numpy.array_split(peptides, num_chunks)
for chunk_peptides in peptide_chunks:
work_items.append({
'alleles': alleles[list(indices)],
'peptides': chunk_peptides,
})
else:
# Same number of chunks for all alleles
num_chunks = int(math.ceil(len(peptides) / args.chunk_size))
print("Splitting peptides into %d chunks" % num_chunks)
peptide_chunks = numpy.array_split(peptides, num_chunks)
work_items = []
for (_, chunk_peptides) in enumerate(peptide_chunks):
work_item = {
'alleles': alleles,
'peptides': chunk_peptides,
}
work_items.append(work_item)
print("Work items: ", len(work_items))
for (i, work_item) in enumerate(work_items):
work_item["work_item_num"] = i
# Combine work items to form tasks.
tasks = []
peptides_in_last_task = None
# We sort work_items to put small items first so they get combined.
for work_item in sorted(work_items, key=lambda d: len(d['peptides'])):
if peptides_in_last_task is not None and (
len(work_item['peptides']) + peptides_in_last_task <
args.chunk_size):
# Add to last task.
tasks[-1]['work_item_dicts'].append(work_item)
peptides_in_last_task += len(work_item['peptides'])
else:
# New task
tasks.append({'work_item_dicts': [work_item]})
peptides_in_last_task = len(work_item['peptides'])
print("Collected %d work items into %d tasks" %
(len(work_items), len(tasks)))
if args.predictor == "mhcflurry":
do_predictions_function = do_predictions_mhcflurry
else:
do_predictions_function = do_predictions_mhctools
worker_pool = None
start = time.time()
if serial_run:
# Serial run
print("Running in serial.")
results = (do_predictions_function(**task) for task in tasks)
elif args.cluster_parallelism:
# Run using separate processes HPC cluster.
print("Running on cluster.")
results = cluster_results_from_args(
args,
work_function=do_predictions_function,
work_items=tasks,
constant_data=GLOBAL_DATA,
input_serialization_method="dill",
result_serialization_method="pickle",
clear_constant_data=True)
else:
worker_pool = worker_pool_with_gpu_assignments_from_args(args)
print("Worker pool", worker_pool)
assert worker_pool is not None
results = worker_pool.imap_unordered(partial(call_wrapped_kwargs,
do_predictions_function),
tasks,
chunksize=1)
allele_to_chunk_index_to_predictions = {}
for allele in alleles:
allele_to_chunk_index_to_predictions[allele] = {}
def write_col(col):
out_path = os.path.join(args.out, col_to_filename[col])
numpy.savez(out_path, result_df[col].values)
print("Wrote [%f%% null]:" % (result_df[col].isnull().mean() * 100.0),
out_path)
print("Writing all columns.")
last_write_time_per_column = {}
for col in result_df.columns:
write_col(col)
last_write_time_per_column[col] = time.time()
print("Done writing all columns. Reading results.")
for worker_results in tqdm.tqdm(results, total=len(work_items)):
for (work_item_num, col_to_predictions) in worker_results:
for (col, predictions) in col_to_predictions.items():
result_df.loc[work_items[work_item_num]['peptides'],
col] = predictions
if time.time() - last_write_time_per_column[col] > 180:
write_col(col)
last_write_time_per_column[col] = time.time()
print("Done processing. Final write for each column.")
for col in result_df.columns:
write_col(col)
if worker_pool:
worker_pool.close()
worker_pool.join()
prediction_time = time.time() - start
print("Done generating predictions in %0.2f min." %
(prediction_time / 60.0))
def normalize_allele_name_or_return_unknown(s):
result = normalize_allele_name(s,
raise_on_error=False,
default_value="UNKNOWN")
imputed_dataset = Dataset.create_empty()
else:
imputed_dataset = dataset.impute_missing_values(imputer)
for allele_name in alleles:
allele_dataset = dataset.get_allele(allele_name)
n_allele = len(allele_dataset)
if allele_name in imputed_dataset.unique_alleles():
imputed_dataset_allele = imputed_dataset.get_allele(allele_name)
else:
imputed_dataset_allele = Dataset.create_empty()
# normalize allele name to check if it's just
allele_name = normalize_allele_name(allele_name)
if allele_name.isdigit():
print("Skipping allele %s" % (allele_name,))
continue
print("\n=== Training predictor for %s: %d samples" % (
allele_name,
n_allele))
model = predictor_from_args(args, allele_name)
json_filename = allele_name + ".json"
json_path = join(args.output_dir, json_filename)
hdf_filename = allele_name + ".hdf"
hdf_path = join(args.output_dir, hdf_filename)
def leave_out_allele_cross_validation(
model,
allele_datasets,
max_ic50,
binary_encoding=False,
n_pretrain_epochs=0,
n_training_epochs=100,
min_samples_per_allele=5,
cv_folds=5,
minibatch_size=128):
"""
Fit the model for every allele in the dataset and return a DataFrame
with the following columns:
allele_name
dataset_size
auc_mean
auc_median
auc_std
auc_min
auc_max
accuracy_mean
accuracy_median
accuracy_std
accuracy_min
accuracy_max
f1_mean
f1_median
f1_std
f1_min
f1_max
"""
scores = ScoreCollection()
X_dict, Y_log_ic50_dict, ic50_dict = encode_allele_datasets(
allele_datasets=allele_datasets,
max_ic50=max_ic50,
binary_encoding=binary_encoding)
initial_weights = [w.copy() for w in model.get_weights()]
for allele_name, dataset in filter_alleles(
allele_datasets, min_samples_per_allele=min_samples_per_allele):
model.set_weights(initial_weights)
X_allele = X_dict[allele_name]
Y_allele = Y_log_ic50_dict[allele_name]
ic50_allele = ic50_dict[allele_name]
if n_pretrain_epochs > 0:
X_other_alleles = np.vstack([
X
for (other_allele, X) in X_dict.items()
if normalize_allele_name(other_allele) != allele_name])
Y_other_alleles = np.concatenate([
y
for (other_allele, y)
in Y_log_ic50_dict.items()
if normalize_allele_name(other_allele) != allele_name])
print("Pre-training X shape: %s" % (X_other_alleles.shape,))
print("Pre-training Y shape: %s" % (Y_other_alleles.shape,))
model.fit(
X_other_alleles,
Y_other_alleles,
nb_epoch=n_pretrain_epochs,
batch_size=minibatch_size,
verbose=0)
print("Cross-validation for %s (%d):" % (allele_name, len(Y_allele)))
aucs, accuracies, f1_scores = kfold_cross_validation_for_single_allele(
allele_name=allele_name,
model=model,
X=X_allele,
Y=Y_allele,
ic50=ic50_allele,
n_training_epochs=n_training_epochs,
cv_folds=cv_folds,
max_ic50=max_ic50,
minibatch_size=minibatch_size)
if len(aucs) == 0:
print("Skipping allele %s" % allele_name)
continue
scores.add(allele_name, auc=aucs, accuracy=accuracies, f1=f1_scores)
return scores.dataframe()
def load_test_data(
dirpaths,
sep="\s+",
ic50_base=10.0,
comment_char="B",
dataset_name="blind"):
"""
Load all allele-specific datasets from the given path assuming filenames
have the form:
pred.PREDICTOR_NAME.CV_METHOD.ALLELE-LENGTH.xls
Example:
pred.netmhc.blind.HLA-A-3201-9.xls
pred.blind.smmpmbec_cpp.Mamu-A-02-9.xls
where ALLELE could be HLA-A-0201 and LENGTH is an integer
Combines all loaded files into a single DataFrame.
If `column_per_predictor` is True then reshape the DataFrame to have
multiple prediction columns, one per distinct predictor.
If ic50_base is not None, then transform IC50 using ic50_base ** pred
"""
# dictionary mapping from (allele, sequence) to dictionary of binding
# predictions and the actual measuremnt called "meas"
test_datasets = {}
predictor_names = set([])
for dirpath in dirpaths:
for filename in listdir(dirpath):
filepath = join(dirpath, filename)
dot_parts = filename.split(".")
dot_parts
if len(dot_parts) != 5:
print("Skipping %s" % filepath)
continue
prefixes = dot_parts[:-2]
interesting_prefixes = {
substring
for substring in prefixes
if substring not in {"pred", "test", dataset_name}
and not substring.startswith("cv_")
}
if len(interesting_prefixes) != 1:
print("Can't infer predictor name for %s" % filepath)
continue
predictor_name = list(interesting_prefixes)[0]
suffix, ext = dot_parts[-2:]
dash_parts = suffix.split("-")
if len(dash_parts) < 2:
print("Skipping %s due to incorrect format" % filepath)
continue
predictor_names.add(predictor_name)
print("Reading %s" % filepath)
allele = normalize_allele_name("-".join(dash_parts[:-1]))
length = int(dash_parts[-1])
df = pd.read_csv(filepath, sep=sep, comment=comment_char)
df["dirpath"] = dirpath
df["predictor"] = predictor_name
df["allele"] = allele
df["length"] = length
if ic50_base is not None:
df["pred"] = ic50_base ** df["pred"]
df["meas"] = ic50_base ** df["meas"]
if allele not in test_datasets:
test_datasets[allele] = defaultdict(OrderedDict)
dataset_dict = test_datasets[allele]
for _, row in df.iterrows():
sequence = row["sequence"]
dataset_dict[sequence]["length"] = length
dataset_dict[sequence]["meas"] = row["meas"]
dataset_dict[sequence][predictor_name] = row["pred"]
test_dataframes = {
allele: pd.DataFrame.from_dict(
ic50_values, orient="index")
for (allele, ic50_values) in test_datasets.items()
}
return test_dataframes, predictor_names
def normalize_allele_name_optional(s):
return normalize_allele_name(s, raise_on_error=False)
