def test_create_model(self):
test_path = EnvironmentSettings.root_path + "test/tmp/w2v_test_tmp/"
PathBuilder.build(test_path)
sequence1 = ReceptorSequence("CASSVFA")
sequence2 = ReceptorSequence("CASSCCC")
metadata1 = {"T1D": "T1D", "subject_id": "1"}
rep1 = Repertoire.build_from_sequence_objects([sequence1, sequence2],
test_path, metadata1)
metadata2 = {"T1D": "CTL", "subject_id": "2"}
rep2 = Repertoire.build_from_sequence_objects([sequence1], test_path,
metadata2)
dataset = RepertoireDataset(repertoires=[rep1, rep2])
model_creator = KmerPairModelCreator()
model = model_creator.create_model(dataset=dataset,
k=2,
vector_size=16,
batch_size=1,
model_path=test_path +
"model.model")
self.assertTrue(isinstance(model, Word2Vec))
self.assertTrue("CA" in model.wv.vocab)
self.assertEqual(400, len(model.wv.vocab))
shutil.rmtree(test_path)
def _construct_test_repertoiredataset(self, path, positional):
receptors1 = ReceptorSequenceList()
receptors2 = ReceptorSequenceList()
if positional:
[
receptors1.append(seq) for seq in [
ReceptorSequence("AAAAAAAAAAAAAAAAA", identifier="1"),
ReceptorSequence("AAAAAAAAAAAAAAAAA", identifier="1")
]
]
[
receptors2.append(seq)
for seq in [ReceptorSequence("TTTTTTTTTTTTT", identifier="1")]
]
else:
[
receptors1.append(seq) for seq in [
ReceptorSequence("AAAA", identifier="1"),
ReceptorSequence("ATA", identifier="2"),
ReceptorSequence("ATA", identifier='3')
]
]
[
receptors2.append(seq) for seq in [
ReceptorSequence("ATA", identifier="1"),
ReceptorSequence("TAA", identifier="2")
]
]
rep1 = Repertoire.build_from_sequence_objects(receptors1,
metadata={
"l1": 1,
"l2": 2,
"subject_id": "1"
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(receptors2,
metadata={
"l1": 0,
"l2": 3,
"subject_id": "2"
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
return dataset, lc
def _create_dummy_data(self, path, dataset_type):
PathBuilder.build(path)
dataset = None
test_repertoire = Repertoire.build(
sequence_aas=[
"DUPDUP", "AILUDGYF", "DFJKHJ", "DIUYUAG", "CTGTCGH"
],
v_genes=["V1-1" for i in range(5)],
j_genes=["J1-1" for i in range(5)],
chains=[
Chain.ALPHA, Chain.BETA, Chain.BETA, Chain.ALPHA, Chain.BETA
],
custom_lists={
"custom_1": [f"CUST-{i}" for i in range(5)],
"custom_2":
[f"CUST-A" for i in range(3)] + [f"CUST-B" for i in range(2)]
},
cell_ids=[1, 1, 1, 2, 2],
path=path)
if dataset_type == "receptor":
receptordataset_filename = f"{path}/receptors.pkl"
with open(receptordataset_filename, "wb") as file:
pickle.dump(test_repertoire.receptors, file)
dataset = ReceptorDataset(filenames=[receptordataset_filename],
identifier="receptor_dataset")
elif dataset_type == "repertoire":
test_repertoire.identifier = "repertoire_dataset"
dataset = RepertoireDataset(repertoires=[test_repertoire])
return dataset
def test_implant_in_repertoire(self):
path = EnvironmentSettings.tmp_test_path + "healthysequenceimplanting/"
PathBuilder.build(path)
repertoire = Repertoire.build_from_sequence_objects(
[
ReceptorSequence(amino_acid_sequence="ACDFQ", identifier="1"),
ReceptorSequence(amino_acid_sequence="TGCDF", identifier="2")
],
path=path,
metadata={"subject_id": "1"})
implanting = HealthySequenceImplanting(
GappedMotifImplanting(),
implanting_computation=ImplantingComputation.ROUND)
signal = Signal("1", [Motif("m1", GappedKmerInstantiation(), "CCC")],
implanting)
repertoire2 = implanting.implant_in_repertoire(repertoire, 0.5, signal,
path)
new_sequences = [
sequence.get_sequence() for sequence in repertoire2.sequences
]
self.assertTrue("ACDFQ" in new_sequences or "TGCDF" in new_sequences)
self.assertTrue(any(["CCC" in sequence for sequence in new_sequences]))
shutil.rmtree(path)
def test_process(self):
path = EnvironmentSettings.root_path + "test/tmp/chain_filter/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects([
ReceptorSequence(
"AAA", metadata=SequenceMetadata(chain="A"), identifier="1")
],
path=path,
metadata={})
rep2 = Repertoire.build_from_sequence_objects([
ReceptorSequence(
"AAC", metadata=SequenceMetadata(chain="B"), identifier="2")
],
path=path,
metadata={})
metadata = pd.DataFrame({"CD": [1, 0]})
metadata.to_csv(path + "metadata.csv")
dataset = RepertoireDataset(repertoires=[rep1, rep2],
metadata_file=path + "metadata.csv")
dataset2 = ChainRepertoireFilter.process(
dataset, {
"keep_chain": "ALPHA",
"result_path": path + "results/"
})
self.assertEqual(1, len(dataset2.get_data()))
self.assertEqual(2, len(dataset.get_data()))
metadata_dict = dataset2.get_metadata(["CD"])
self.assertEqual(1, len(metadata_dict["CD"]))
self.assertEqual(1, metadata_dict["CD"][0])
for rep in dataset2.get_data():
self.assertEqual("AAA", rep.sequences[0].get_sequence())
self.assertRaises(AssertionError, ChainRepertoireFilter.process,
dataset, {
"keep_chain": "GAMMA",
"result_path": path + "results/"
})
shutil.rmtree(path)
def test_run(self):
path = EnvironmentSettings.root_path + "test/tmp/dataencoder/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("AAA", identifier="1")],
metadata={
"l1": 1,
"l2": 2
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("ATA", identifier="2")],
metadata={
"l1": 0,
"l2": 3
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": ModelType.SEQUENCE.name,
"vector_size": 6
})
res = DataEncoder.run(
DataEncoderParams(dataset=dataset,
encoder=encoder,
encoder_params=EncoderParams(
model={},
pool_size=2,
label_config=lc,
result_path=path,
filename="dataset.csv"),
store_encoded_data=False))
self.assertTrue(isinstance(res, RepertoireDataset))
self.assertTrue(res.encoded_data.examples.shape[0] == 2)
shutil.rmtree(path)
def test_encode(self):
test_path = EnvironmentSettings.root_path + "test/tmp/w2v/"
PathBuilder.build(test_path)
sequence1 = ReceptorSequence("CASSVFA", identifier="1")
sequence2 = ReceptorSequence("CASSCCC", identifier="2")
metadata1 = {"T1D": "T1D", "subject_id": "1"}
rep1 = Repertoire.build_from_sequence_objects([sequence1, sequence2],
test_path, metadata1)
metadata2 = {"T1D": "CTL", "subject_id": "2"}
rep2 = Repertoire.build_from_sequence_objects([sequence1], test_path,
metadata2)
dataset = RepertoireDataset(repertoires=[rep1, rep2])
label_configuration = LabelConfiguration()
label_configuration.add_label("T1D", ["T1D", "CTL"])
config_params = EncoderParams(model={},
learn_model=True,
result_path=test_path,
label_config=label_configuration,
filename="dataset.pkl")
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": "sequence",
"vector_size": 16
})
encoded_dataset = encoder.encode(dataset=dataset, params=config_params)
self.assertIsNotNone(encoded_dataset.encoded_data)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0] == 2)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[1] == 16)
self.assertTrue(len(encoded_dataset.encoded_data.labels["T1D"]) == 2)
self.assertTrue(encoded_dataset.encoded_data.labels["T1D"][0] == "T1D")
self.assertTrue(isinstance(encoder, W2VRepertoireEncoder))
shutil.rmtree(test_path)
def process_repertoire(repertoire: Repertoire, params: dict) -> Repertoire:
data = pd.DataFrame(repertoire.load_data())
groupby_fields = DuplicateSequenceFilter._prepare_group_by_field(
params, data.columns)
custom_lists = list(set(data.columns) - set(Repertoire.FIELDS))
agg_dict = DuplicateSequenceFilter._prepare_agg_dict(
params, data.columns, custom_lists)
# Chain objects can not be aggregated, convert to strings
if "chains" in data.columns:
data["chains"] = [
chain.value if isinstance(chain, Chain) else chain
for chain in data["chains"]
]
else:
data["chains"] = None
no_duplicates = data.groupby(groupby_fields).agg(
agg_dict).reset_index()
processed_repertoire = Repertoire.build(
sequence_aas=list(no_duplicates["sequence_aas"])
if "sequence_aas" in no_duplicates.columns else None,
sequences=list(no_duplicates["sequences"])
if "sequences" in no_duplicates.columns else None,
v_genes=list(no_duplicates["v_genes"])
if "v_genes" in no_duplicates.columns else None,
j_genes=list(no_duplicates["j_genes"])
if 'j_genes' in no_duplicates.columns else None,
chains=[Chain(key) for key in list(no_duplicates["chains"])]
if "chains" in no_duplicates.columns else None,
counts=list(no_duplicates["counts"])
if "counts" in no_duplicates else None,
region_types=list(no_duplicates["region_types"])
if "region_types" in no_duplicates else None,
custom_lists={
key: list(no_duplicates[key])
for key in custom_lists
},
sequence_identifiers=list(no_duplicates["sequence_identifiers"]),
metadata=copy.deepcopy(repertoire.metadata),
path=params["result_path"])
return processed_repertoire
def test_match(self):
path = EnvironmentSettings.root_path + "test/tmp/seqmatch/"
PathBuilder.build(path)
repertoire = Repertoire.build_from_sequence_objects(
sequence_objects=[
ReceptorSequence(amino_acid_sequence="AAAAAA",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J2"),
identifier="3"),
ReceptorSequence(amino_acid_sequence="CCCCCC",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J2"),
identifier="4"),
ReceptorSequence(amino_acid_sequence="AAAACC",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J2"),
identifier="5"),
ReceptorSequence(amino_acid_sequence="TADQVF",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J3"),
identifier="6")
],
metadata={"CD": True},
path=path)
dataset = RepertoireDataset(repertoires=[repertoire])
sequences = [
ReceptorSequence("AAAACA",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J2"),
identifier="1"),
ReceptorSequence("TADQV",
metadata=SequenceMetadata(chain="A",
v_gene="V1",
j_gene="J3"),
identifier="2")
]
matcher = SequenceMatcher()
result = matcher.match(dataset, sequences, 2,
SequenceMatchingSummaryType.PERCENTAGE)
self.assertTrue("repertoires" in result)
self.assertEqual(
1,
len(result["repertoires"][0]["sequences"][3]
["matching_sequences"]))
self.assertTrue(result["repertoires"][0]["metadata"]["CD"])
self.assertEqual(1, len(result["repertoires"]))
shutil.rmtree(path)
def test_match_repertoire(self):
path = EnvironmentSettings.root_path + "test/tmp/seqmatchrep/"
PathBuilder.build(path)
repertoire = Repertoire.build_from_sequence_objects(sequence_objects=[
ReceptorSequence(amino_acid_sequence="AAAAAA",
identifier="1",
metadata=SequenceMetadata(chain="A", count=3)),
ReceptorSequence(amino_acid_sequence="CCCCCC",
identifier="2",
metadata=SequenceMetadata(chain="A", count=2)),
ReceptorSequence(amino_acid_sequence="AAAACC",
identifier="3",
metadata=SequenceMetadata(chain="A", count=1)),
ReceptorSequence(amino_acid_sequence="TADQVF",
identifier="4",
metadata=SequenceMetadata(chain="A", count=4))
],
metadata={
"CD": True
},
path=path)
sequences = [
ReceptorSequence("AAAACA", metadata=SequenceMetadata(chain="A")),
ReceptorSequence("TADQV", metadata=SequenceMetadata(chain="A"))
]
matcher = SequenceMatcher()
result = matcher.match_repertoire(repertoire, 0, sequences, 2,
SequenceMatchingSummaryType.COUNT)
self.assertTrue("sequences" in result)
self.assertTrue("repertoire" in result)
self.assertTrue("repertoire_index" in result)
self.assertEqual(4, len(result["sequences"]))
self.assertEqual(1, len(result["sequences"][0]["matching_sequences"]))
self.assertEqual(0, len(result["sequences"][1]["matching_sequences"]))
self.assertEqual(1, len(result["sequences"][2]["matching_sequences"]))
self.assertEqual(1, len(result["sequences"][3]["matching_sequences"]))
self.assertEqual(
3,
len([
r for r in result["sequences"]
if len(r["matching_sequences"]) > 0
]))
self.assertTrue(result["metadata"]["CD"])
result = matcher.match_repertoire(
repertoire, 0, sequences, 2,
SequenceMatchingSummaryType.CLONAL_PERCENTAGE)
self.assertEqual(0.8, result["clonal_percentage"])
shutil.rmtree(path)
def _process_repertoire(index, repertoire, current_implanting, simulation_state) -> Repertoire:
if current_implanting is not None:
return SignalImplanter._implant_in_repertoire(index, repertoire, current_implanting, simulation_state)
else:
new_repertoire = Repertoire.build_from_sequence_objects(repertoire.sequences, simulation_state.result_path + "repertoires/",
repertoire.metadata)
for signal in simulation_state.signals:
new_repertoire.metadata[f"signal_{signal.id}"] = False
return new_repertoire
def test_implant_in_repertoire(self):
path = PathBuilder.build(f"{EnvironmentSettings.tmp_test_path}full_seq_implanting/")
signal = Signal("sig1", [Motif("motif1", GappedKmerInstantiation(max_gap=0), "AAAA")], FullSequenceImplanting())
repertoire = Repertoire.build(["CCCC", "CCCC", "CCCC"], path=path)
new_repertoire = signal.implant_to_repertoire(repertoire, 0.33, path)
self.assertEqual(len(repertoire.sequences), len(new_repertoire.sequences))
self.assertEqual(1, len([seq for seq in new_repertoire.sequences if seq.amino_acid_sequence == "AAAA"]))
self.assertEqual(2, len([seq for seq in new_repertoire.sequences if seq.amino_acid_sequence == "CCCC"]))
shutil.rmtree(path)
def implant_in_repertoire(self, repertoire: Repertoire, repertoire_implanting_rate: float, signal, path):
assert all("/" not in motif.seed for motif in signal.motifs), \
f'FullSequenceImplanting: motifs cannot include gaps. Check motifs {[motif.identifier for motif in signal.motifs]}.'
sequences = repertoire.sequences
new_sequence_count = math.ceil(len(sequences) * repertoire_implanting_rate)
assert new_sequence_count > 0, \
f"FullSequenceImplanting: there are too few sequences ({len(sequences)}) in the repertoire with identifier {repertoire.identifier} " \
f"to have the given repertoire implanting rate ({repertoire_implanting_rate}). Please consider increasing the repertoire implanting rate."
new_sequences = self._create_new_sequences(sequences, new_sequence_count, signal)
metadata = copy.deepcopy(repertoire.metadata)
metadata[f"signal_{signal.id}"] = True
return Repertoire.build_from_sequence_objects(new_sequences, path, metadata)
def test_create_sentences_from_repertoire(self):
path = EnvironmentSettings.tmp_test_path + "kmer/"
PathBuilder.build(path)
rep = Repertoire.build_from_sequence_objects([ReceptorSequence(amino_acid_sequence="AACT"),
ReceptorSequence(amino_acid_sequence="ACCT"),
ReceptorSequence(amino_acid_sequence="AACT")], path, {})
sentences = KmerHelper.create_sentences_from_repertoire(rep, 3)
self.assertEqual(3, len(sentences))
self.assertTrue(len(sentences[0]) == 2 and "AAC" in sentences[0] and "ACT" in sentences[0])
shutil.rmtree(path)
def _build_new_repertoire(self, sequences, repertoire_metadata, signal,
path) -> Repertoire:
if repertoire_metadata is not None:
metadata = copy.deepcopy(repertoire_metadata)
else:
metadata = {}
# when adding implant to a repertoire, only signal id is stored:
# more detailed information is available in each receptor_sequence
# (specific motif and motif instance)
metadata[f"signal_{signal.id}"] = True
repertoire = Repertoire.build_from_sequence_objects(
sequences, path, metadata)
return repertoire
def _repertoire_to_dataframe(repertoire: Repertoire, region_type):
# get all fields (including custom fields)
df = pd.DataFrame(repertoire.load_data())
for column in ['v_alleles', 'j_alleles', 'v_genes', 'j_genes']:
if column not in df.columns:
df.loc[:, column] = None
AIRRExporter.update_gene_columns(df, 'alleles', 'genes')
# rename mandatory fields for airr-compliance
mapper = {"sequence_identifiers": "sequence_id", "v_alleles": "v_call", "j_alleles": "j_call", "chains": "locus", "counts": "duplicate_count",
"sequences": AIRRExporter.get_sequence_field(region_type), "sequence_aas": AIRRExporter.get_sequence_aa_field(region_type)}
df = df.rename(mapper=mapper, axis="columns")
return df
def load_repertoire_as_object(import_class, metadata_row, params: DatasetImportParams):
try:
alternative_load_func = getattr(import_class, "alternative_load_func", None)
dataframe = ImportHelper.load_sequence_dataframe(f"{params.path}{metadata_row['filename']}", params, alternative_load_func)
dataframe = import_class.preprocess_dataframe(dataframe, params)
sequence_lists = {field: dataframe[field].values.tolist() for field in Repertoire.FIELDS if field in dataframe.columns}
sequence_lists["custom_lists"] = {field: dataframe[field].values.tolist()
for field in list(set(dataframe.columns) - set(Repertoire.FIELDS))}
repertoire_inputs = {**{"metadata": metadata_row.to_dict(), "path": params.result_path + "repertoires/"}, **sequence_lists}
repertoire = Repertoire.build(**repertoire_inputs)
return repertoire
except Exception as exception:
raise RuntimeError(f"{ImportHelper.__name__}: error when importing file {metadata_row['filename']}.") from exception
def build(sequences: list, path: str, labels: dict = None, seq_metadata: list = None, subject_ids: list = None):
if subject_ids is not None:
assert len(subject_ids) == len(sequences)
if seq_metadata is not None:
assert len(sequences) == len(seq_metadata)
for index, sequence_list in enumerate(sequences):
assert len(sequence_list) == len(seq_metadata[index])
PathBuilder.build(path)
rep_path = PathBuilder.build(path + "repertoires/")
repertoires = []
if subject_ids is None:
subject_ids = []
for rep_index, sequence_list in enumerate(sequences):
rep_sequences = ReceptorSequenceList()
if len(subject_ids) < len(sequences):
subject_ids.append("rep_" + str(rep_index))
for seq_index, sequence in enumerate(sequence_list):
if seq_metadata is None:
m = SequenceMetadata(v_subgroup="TRBV1", v_gene="TRBV1-1", v_allele="TRBV1-1*01", j_subgroup="TRBJ1", j_gene="TRBJ1-1", j_allele="TRBJ1-1*01", count=1, chain="TRB", region_type="IMGT_CDR3")
else:
m = SequenceMetadata(**seq_metadata[rep_index][seq_index])
s = ReceptorSequence(amino_acid_sequence=sequence, metadata=m, identifier=str(seq_index))
rep_sequences.append(s)
if labels is not None:
metadata = {key: labels[key][rep_index] for key in labels.keys()}
else:
metadata = {}
metadata = {**metadata, **{"subject_id": subject_ids[rep_index]}}
repertoire = Repertoire.build_from_sequence_objects(rep_sequences, rep_path, metadata)
repertoires.append(repertoire)
df = pd.DataFrame({**{"filename": [f"{repertoire.identifier}_data.npy" for repertoire in repertoires], "subject_id": subject_ids,
"repertoire_identifier": [repertoire.identifier for repertoire in repertoires]},
**(labels if labels is not None else {})})
df.to_csv(path + "metadata.csv", index=False)
return repertoires, path + "metadata.csv"
def create_dummy_repertoire(self, path):
sequence_objects = [
ReceptorSequence(amino_acid_sequence="AAA",
nucleotide_sequence="GCTGCTGCT",
identifier="receptor_1",
metadata=SequenceMetadata(v_gene="TRBV1",
j_gene="TRBJ1",
chain=Chain.BETA,
count=5,
region_type="IMGT_CDR3",
frame_type="IN",
custom_params={
"d_call": "TRBD1",
"custom_test":
"cust1"
})),
ReceptorSequence(amino_acid_sequence="GGG",
nucleotide_sequence="GGTGGTGGT",
identifier="receptor_2",
metadata=SequenceMetadata(v_gene="TRAV2",
v_allele="TRAV2*01",
j_gene="TRAJ2",
chain=Chain.ALPHA,
count=15,
frame_type=None,
region_type="IMGT_CDR3",
custom_params={
"d_call": "TRAD2",
"custom_test":
"cust2"
}))
]
repertoire = Repertoire.build_from_sequence_objects(
sequence_objects=sequence_objects,
path=path,
metadata={"subject_id": "REP1"})
df = pd.DataFrame({
"filename": [f"{repertoire.identifier}_data.npy"],
"subject_id": ["1"],
"repertoire_identifier": [repertoire.identifier]
})
df.to_csv(path + "metadata.csv", index=False)
return repertoire, path + "metadata.csv"
def test_run(self):
r = []
path = EnvironmentSettings.root_path + "test/tmp/signalImplanter/"
if not os.path.isdir(path):
os.makedirs(path)
sequences = [ReceptorSequence("ACDEFG", identifier="1"), ReceptorSequence("ACDEFG", identifier="2"),
ReceptorSequence("ACDEFG", identifier="3"), ReceptorSequence("ACDEFG", identifier="4")]
for i in range(10):
rep = Repertoire.build_from_sequence_objects(sequence_objects=sequences, path=path, metadata={})
r.append(rep)
dataset = RepertoireDataset(repertoires=r)
m1 = Motif(identifier="m1", instantiation=GappedKmerInstantiation(), seed="CAS")
m2 = Motif(identifier="m2", instantiation=GappedKmerInstantiation(), seed="CCC")
s1 = Signal(identifier="s1", motifs=[m1], implanting_strategy=HealthySequenceImplanting(GappedMotifImplanting(), implanting_computation=ImplantingComputation.ROUND))
s2 = Signal(identifier="s2", motifs=[m1, m2],
implanting_strategy=HealthySequenceImplanting(GappedMotifImplanting(), implanting_computation=ImplantingComputation.ROUND))
simulation = Simulation([Implanting(dataset_implanting_rate=0.2, repertoire_implanting_rate=0.5, signals=[s1, s2], name="i1"),
Implanting(dataset_implanting_rate=0.2, repertoire_implanting_rate=0.5, signals=[s2], name="i2")])
input_params = SimulationState(dataset=dataset, result_path=path, simulation=simulation, signals=[s1, s2])
new_dataset = SignalImplanter.run(input_params)
reps_with_s2 = sum([rep.metadata[f"signal_{s2.id}"] is True for rep in new_dataset.get_data(batch_size=10)])
reps_with_s1 = sum([rep.metadata[f"signal_{s1.id}"] is True for rep in new_dataset.get_data(batch_size=10)])
self.assertEqual(10, len(new_dataset.get_example_ids()))
self.assertTrue(all([f"signal_{s1.id}" in rep.metadata.keys() for rep in new_dataset.get_data(batch_size=10)]))
self.assertTrue(all([f"signal_{s2.id}" in rep.metadata.keys() for rep in new_dataset.get_data(batch_size=10)]))
self.assertTrue(reps_with_s2 == 4)
self.assertTrue(reps_with_s1 == 2)
self.assertEqual(10, len(new_dataset.get_example_ids()))
metadata_filenames = new_dataset.get_metadata(["filename"])["filename"]
self.assertTrue(all([repertoire.data_filename in metadata_filenames for repertoire in new_dataset.repertoires]))
shutil.rmtree(path)
def test_find_label_associated_sequence_p_values(self):
path = EnvironmentSettings.tmp_test_path + "comparison_data_find_label_assocseqpvalues/"
PathBuilder.build(path)
repertoires = [Repertoire.build_from_sequence_objects([ReceptorSequence()], path, {
"l1": val, "subject_id": subject_id
}) for val, subject_id in zip([True, True, False, False], ["rep_0", "rep_1", "rep_2", "rep_3"])]
col_name_index = {repertoires[index].identifier: index for index in range(len(repertoires))}
comparison_data = ComparisonData(repertoire_ids=[repertoire.identifier for repertoire in repertoires],
comparison_attributes=["sequence_aas"], sequence_batch_size=4, path=path)
comparison_data.batches = [ComparisonDataBatch(**{'matrix': np.array([[1., 0., 0., 0.],
[1., 1., 0., 0.]]),
'items': [('GGG',), ('III',)],
'repertoire_index_mapping': col_name_index, 'path': path, 'identifier': 0}),
ComparisonDataBatch(**{'matrix': np.array([[1., 1., 0., 1.],
[1., 1., 1., 1.]]),
'items': [('LLL',), ('MMM',)],
'repertoire_index_mapping': col_name_index, 'path': path, 'identifier': 1}),
ComparisonDataBatch(**{'matrix': np.array([[0., 1., 0., 0.],
[0., 1., 0., 1.]]),
'items': [('DDD',), ('EEE',)],
'repertoire_index_mapping': col_name_index, 'path': path, 'identifier': 2}),
ComparisonDataBatch(**{'matrix': np.array([[0., 1., 1., 1.],
[0., 0., 1., 1.]]),
'items': [('FFF',), ('CCC',)],
'repertoire_index_mapping': col_name_index, 'path': path, 'identifier': 3}),
ComparisonDataBatch(**{'matrix': np.array([[0., 0., 0., 1.]]),
'items': [('AAA',)],
'repertoire_index_mapping': col_name_index, 'path': path, 'identifier': 4})]
p_values = SequenceFilterHelper.find_label_associated_sequence_p_values(comparison_data, repertoires, Label('l1', [True, False], positive_class=True))
print(p_values)
self.assertTrue(
np.allclose([SequenceFilterHelper.INVALID_P_VALUE, 0.1666666666666667, 0.5000000000000001, 1., SequenceFilterHelper.INVALID_P_VALUE,
0.8333333333333331, 1., 1., 2], p_values, equal_nan=True))
shutil.rmtree(path)
def test_receptor(self):
path = EnvironmentSettings.tmp_test_path + "receptortestingpathrepertoire/"
PathBuilder.build(path)
sequences = [
ReceptorSequence(amino_acid_sequence="AAA",
identifier="1",
metadata=SequenceMetadata(v_gene="V1",
cell_id="1",
chain=Chain.ALPHA,
custom_params={
"cmv": "no",
"coeliac": False
})),
ReceptorSequence(amino_acid_sequence="CCC",
identifier="2",
metadata=SequenceMetadata(j_gene="J1",
cell_id="1",
chain=Chain.BETA,
custom_params={
"cmv": "yes",
"coeliac": True
})),
ReceptorSequence(amino_acid_sequence="FFF",
identifier="3",
metadata=SequenceMetadata(v_gene="V1",
cell_id="1",
chain=Chain.ALPHA,
custom_params={
"cmv": "no",
"coeliac": False
})),
ReceptorSequence(amino_acid_sequence="EEE",
identifier="4",
metadata=SequenceMetadata(j_gene="J1",
cell_id="1",
chain=Chain.BETA,
custom_params={
"cmv": "yes",
"coeliac": True
})),
ReceptorSequence(amino_acid_sequence="FFF",
identifier="5",
metadata=SequenceMetadata(v_gene="V1",
cell_id="2",
chain=Chain.GAMMA,
custom_params={
"cmv": "no",
"coeliac": False
})),
ReceptorSequence(amino_acid_sequence="EEE",
identifier="6",
metadata=SequenceMetadata(j_gene="J1",
cell_id="2",
chain=Chain.DELTA,
custom_params={
"cmv": "yes",
"coeliac": True
})),
ReceptorSequence(amino_acid_sequence="EEE",
identifier="7",
metadata=SequenceMetadata(j_gene="J2",
cell_id="2",
chain=Chain.DELTA,
custom_params={
"cmv": "yes",
"coeliac": True
}))
]
obj = Repertoire.build_from_sequence_objects(sequences, path, {
"cmv": "yes",
'subject_id': "1"
})
receptors = obj.receptors
self.assertEqual(6, len(receptors))
cells = obj.cells
self.assertEqual(2, len(cells))
shutil.rmtree(path)
class TestDataSummarizer(TestCase):
# 5 features, 3 repertoires. Each repertoire has 3 labels. Each feature has 2 annotations.
encoded_data_1 = {
'examples':
sparse.csr_matrix(
np.array([[1, 2, 3, 4, 5], [0, 0, 0, 1, 1], [1, 1, 0, 0, 0]])),
'example_ids': ['rep1', 'rep2', 'rep3'],
'labels': {
"diabetes": ['diabetes pos', 'diabetes neg', 'diabetes neg'],
"celiac": ['celiac pos', 'celiac pos', 'celiac pos'],
"cmv": ['cmv pos', 'cmv neg', 'cmv pos']
},
'feature_names': ['a', 'b', 'c', 'd', 'e'],
'feature_annotations':
pd.DataFrame({
"specificity": ["cmv", "ebv", "cmv", "gluten", "gluten"],
"p_val": [0.01, 0.00001, 0.1, 0, 0.0000001]
})
}
dataset_1 = RepertoireDataset(encoded_data=EncodedData(**encoded_data_1),
repertoires=[
Repertoire("1.npy", None, "1"),
Repertoire("2.npy", None, "2"),
Repertoire("3.npy", None, "3")
])
encoded_data_2 = {
'examples':
sparse.csr_matrix(
np.array([[1, 2, 3, 4, 5], [0, 0, 0, 1, 1], [1, 1, 0, 0, 0],
[90, 10, 1, 3, 4], [0, 1, 1, 100, 200]])),
'example_ids': ['rep1', 'rep2', 'rep3', 'rep4', 'rep5'],
'labels': {
"diabetes": [
'diabetes pos', 'diabetes neg', 'diabetes neg', 'diabetes pos',
'diabetes pos'
],
"celiac": [
'celiac pos', 'celiac pos', 'celiac pos', 'celiac neg',
'celiac pos'
],
"cmv": ['cmv pos', 'cmv neg', 'cmv pos', 'cmv pos', 'cmv neg']
},
'feature_names': ['a', 'b', 'c', 'd', 'e'],
'feature_annotations':
pd.DataFrame({
"specificity": ["cmv", "ebv", "cmv", "gluten", "gluten"],
"something": ["a", "b", "b", "a", "a"],
"p_val": [0.01, 0.00001, 0.1, 0, 0.0000001]
})
}
dataset_2 = RepertoireDataset(encoded_data=EncodedData(**encoded_data_2))
def setUp(self) -> None:
os.environ[Constants.CACHE_TYPE] = CacheType.TEST.name
def test_filter_repertoires(self):
dataset = TestDataSummarizer.dataset_1
criteria = {
"type":
BooleanType.AND,
"operands": [{
"type": OperationType.IN,
"allowed_values": ["celiac pos"],
"value": {
"type": DataType.COLUMN,
"name": "celiac"
}
}, {
"type": OperationType.IN,
"allowed_values": ["cmv pos"],
"value": {
"type": DataType.COLUMN,
"name": "cmv"
}
}]
}
filtered = DataSummarizer.filter_repertoires(dataset, criteria)
self.assertTrue(filtered.get_example_count() == 2)
self.assertTrue(filtered.encoded_data.examples.shape[0] == 2)
self.assertTrue(filtered.encoded_data.examples.shape[1] == 5)
def test_filter_features(self):
dataset = TestDataSummarizer.dataset_1
criteria = {
"type":
BooleanType.OR,
"operands": [{
"type": OperationType.IN,
"allowed_values": ["gluten"],
"value": {
"type": DataType.COLUMN,
"name": "specificity"
}
}, {
"type": OperationType.LESS_THAN,
"threshold": 0.0001,
"value": {
"type": DataType.COLUMN,
"name": "p_val"
}
}]
}
filtered = DataSummarizer.filter_features(dataset, criteria)
self.assertEqual(3, filtered.get_example_count())
self.assertTrue(filtered.encoded_data.examples.shape[0] == 3)
self.assertTrue(filtered.encoded_data.examples.shape[1] == 3)
def test_annotate_repertoires(self):
dataset = TestDataSummarizer.dataset_1
criteria = {
"type":
BooleanType.AND,
"operands": [{
"type": OperationType.IN,
"allowed_values": ["celiac pos"],
"value": {
"type": DataType.COLUMN,
"name": "celiac"
}
}, {
"type": OperationType.IN,
"allowed_values": ["cmv pos"],
"value": {
"type": DataType.COLUMN,
"name": "cmv"
}
}]
}
annotated = DataSummarizer.annotate_repertoires(
dataset, criteria, "annotate")
self.assertTrue(annotated.encoded_data.examples.shape[0] == 3)
self.assertTrue(annotated.encoded_data.examples.shape[1] == 5)
def test_annotate_features(self):
dataset = TestDataSummarizer.dataset_1
criteria = {
"type":
BooleanType.OR,
"operands": [{
"type": OperationType.IN,
"allowed_values": ["gluten"],
"value": {
"type": DataType.COLUMN,
"name": "specificity"
}
}, {
"type": OperationType.LESS_THAN,
"threshold": 0.0001,
"value": {
"type": DataType.COLUMN,
"name": "p_val"
}
}]
}
annotated = DataSummarizer.annotate_features(dataset, criteria,
"annotate")
self.assertTrue(annotated.encoded_data.examples.shape[0] == 3)
self.assertTrue(annotated.encoded_data.examples.shape[1] == 5)
def test_annotate_features_2(self):
dataset = TestDataSummarizer.dataset_1
criteria = {
"type": OperationType.IN,
"allowed_values": ["gluten"],
"value": {
"type": DataType.COLUMN,
"name": "specificity"
}
}
annotated = DataSummarizer.annotate_features(dataset, criteria,
"annotate")
self.assertTrue(annotated.encoded_data.examples.shape[0] == 3)
self.assertTrue(annotated.encoded_data.examples.shape[1] == 5)
def test_encode(self):
path = EnvironmentSettings.root_path + "test/tmp/kmerfreqenc/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects([ReceptorSequence("AAA", identifier="1"),
ReceptorSequence("ATA", identifier="2"),
ReceptorSequence("ATA", identifier='3')],
metadata={"l1": 1, "l2": 2, "subject_id": "1"}, path=path)
rep2 = Repertoire.build_from_sequence_objects([ReceptorSequence("ATA", identifier="1"),
ReceptorSequence("TAA", identifier="2"),
ReceptorSequence("AAC", identifier="3")],
metadata={"l1": 0, "l2": 3, "subject_id": "2"}, path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.IDENTITY.name,
"k": 3
})
d1 = encoder.encode(dataset, EncoderParams(
result_path=path + "1/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
encoder = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
})
d2 = encoder.encode(dataset, EncoderParams(
result_path=path + "2/",
label_config=lc,
pool_size=2,
learn_model=True,
model={},
filename="dataset.csv"
))
encoder3 = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.BINARY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
})
d3 = encoder3.encode(dataset, EncoderParams(
result_path=path + "3/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
shutil.rmtree(path)
self.assertTrue(isinstance(d1, RepertoireDataset))
self.assertTrue(isinstance(d2, RepertoireDataset))
self.assertEqual(0.67, np.round(d2.encoded_data.examples[0, 2], 2))
self.assertEqual(0.0, np.round(d3.encoded_data.examples[0, 1], 2))
self.assertTrue(isinstance(encoder, KmerFrequencyEncoder))
def test_encode(self):
path = EnvironmentSettings.root_path + "test/tmp/evennessenc/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects(sequence_objects=[
ReceptorSequence("AAA", metadata=SequenceMetadata(count=10))
for i in range(1000)
] + [
ReceptorSequence("AAA", metadata=SequenceMetadata(count=100))
for i in range(1000)
] + [
ReceptorSequence("AAA", metadata=SequenceMetadata(count=1))
for i in range(1000)
],
metadata={
"l1": "test_1",
"l2": 2
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(sequence_objects=[
ReceptorSequence("AAA", metadata=SequenceMetadata(count=10))
for i in range(1000)
],
metadata={
"l1": "test_2",
"l2": 3
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", ["test_1", "test_2"])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = EvennessProfileEncoder.build_object(
dataset, **{
"min_alpha": 0,
"max_alpha": 10,
"dimension": 51
})
d1 = encoder.encode(
dataset, EncoderParams(
result_path=path + "1/",
label_config=lc,
))
encoder = EvennessProfileEncoder.build_object(
dataset, **{
"min_alpha": 0,
"max_alpha": 10,
"dimension": 11
})
d2 = encoder.encode(
dataset,
EncoderParams(result_path=path, label_config=lc, pool_size=2))
self.assertAlmostEqual(d1.encoded_data.examples[0, 0], 1)
self.assertAlmostEqual(d1.encoded_data.examples[0, 1], 0.786444)
self.assertAlmostEqual(d1.encoded_data.examples[1, 0], 1)
self.assertAlmostEqual(d1.encoded_data.examples[1, 1], 1)
shutil.rmtree(path)
def test_process(self):
path = EnvironmentSettings.root_path + "test/tmp/duplicatesequencefilter/"
PathBuilder.build(path)
dataset = RepertoireDataset(repertoires=[
Repertoire.build(
sequence_aas=["AAA", "AAA", "CCC", "AAA", "CCC", "CCC", "CCC"],
sequences=[
"ntAAA", "ntBBB", "ntCCC", "ntAAA", "ntCCC", "ntCCC",
"ntDDD"
],
v_genes=["v1", "v1", "v1", "v1", "v1", "v1", "v1"],
j_genes=["j1", "j1", "j1", "j1", "j1", "j1", "j1"],
chains=[
Chain.ALPHA, Chain.ALPHA, Chain.ALPHA, Chain.ALPHA,
Chain.ALPHA, Chain.ALPHA, Chain.BETA
],
counts=[10, 20, 30, 5, 20, None, 40],
region_types=[
"IMGT_CDR3", "IMGT_CDR3", "IMGT_CDR3", "IMGT_CDR3",
"IMGT_CDR3", "IMGT_CDR3", "IMGT_CDR3"
],
custom_lists={
"custom1": ["yes", "yes", "yes", "no", "no", "no", "no"],
"custom2": ["yes", "yes", "yes", "no", "no", "no", "no"]
},
sequence_identifiers=[1, 2, 3, 4, 5, 6, 7],
path=path)
])
# collapse by amino acids & use sum counts
dupfilter = DuplicateSequenceFilter(
filter_sequence_type=SequenceType.AMINO_ACID,
count_agg=CountAggregationFunction.SUM,
batch_size=4)
reduced_repertoire = dupfilter.process_dataset(
dataset=dataset, result_path=path).repertoires[0]
attr = reduced_repertoire.get_attributes([
"sequence_identifiers", "sequence_aas", "sequences", "counts",
"chains"
])
self.assertEqual(3, len(reduced_repertoire.get_sequence_identifiers()))
self.assertListEqual(["AAA", "CCC", "CCC"], list(attr["sequence_aas"]))
self.assertListEqual(["ntAAA", "ntCCC", "ntDDD"],
list(attr["sequences"]))
self.assertListEqual([35, 50, 40], list(attr["counts"]))
self.assertListEqual([1, 3, 7], list(attr["sequence_identifiers"]))
self.assertListEqual(
[Chain.get_chain("A"),
Chain.get_chain("A"),
Chain.get_chain('B')], list(attr["chains"]))
# collapse by nucleotides & use min counts
dupfilter = DuplicateSequenceFilter(
filter_sequence_type=SequenceType.NUCLEOTIDE,
count_agg=CountAggregationFunction.MIN,
batch_size=4)
reduced_repertoire = dupfilter.process_dataset(
dataset=dataset, result_path=path).repertoires[0]
attr = reduced_repertoire.get_attributes(
["sequence_identifiers", "sequence_aas", "sequences", "counts"])
self.assertEqual(4, len(reduced_repertoire.get_sequence_identifiers()))
self.assertListEqual([1, 2, 3, 7], list(attr["sequence_identifiers"]))
self.assertListEqual(["AAA", "AAA", "CCC", "CCC"],
list(attr["sequence_aas"]))
self.assertListEqual(["ntAAA", "ntBBB", "ntCCC", "ntDDD"],
list(attr["sequences"]))
self.assertListEqual([5, 20, 20, 40], list(attr["counts"]))
shutil.rmtree(path)
def test_run(self):
dataset = RepertoireDataset(repertoires=[
Repertoire("0.npy", "", "0"),
Repertoire("0.npy", "", "1"),
Repertoire("0.npy", "", "2"),
Repertoire("0.npy", "", "3"),
Repertoire("0.npy", "", "4"),
Repertoire("0.npy", "", "5"),
Repertoire("0.npy", "", "6"),
Repertoire("0.npy", "", "7")
])
paths = [
EnvironmentSettings.root_path +
"test/tmp/datasplitter/split_{}".format(i) for i in range(5)
]
for path in paths:
PathBuilder.build(path)
df = pd.DataFrame(data={
"key1": [0, 0, 1, 1, 1, 2, 2, 0],
"filename": [0, 1, 2, 3, 4, 5, 6, 7]
})
df.to_csv(EnvironmentSettings.root_path +
"test/tmp/datasplitter/metadata.csv")
dataset.metadata_file = EnvironmentSettings.root_path + "test/tmp/datasplitter/metadata.csv"
training_percentage = 0.7
trains, tests = DataSplitter.run(
DataSplitterParams(dataset=dataset,
training_percentage=training_percentage,
split_strategy=SplitType.RANDOM,
split_count=5,
paths=paths))
self.assertTrue(isinstance(trains[0], RepertoireDataset))
self.assertTrue(isinstance(tests[0], RepertoireDataset))
self.assertEqual(len(trains[0].get_data()), 5)
self.assertEqual(len(tests[0].get_data()), 3)
self.assertEqual(5, len(trains))
self.assertEqual(5, len(tests))
self.assertEqual(5, len(trains[0].repertoires))
trains2, tests2 = DataSplitter.run(
DataSplitterParams(dataset=dataset,
training_percentage=training_percentage,
split_strategy=SplitType.RANDOM,
split_count=5,
paths=paths))
self.assertEqual(trains[0].get_repertoire_ids(),
trains2[0].get_repertoire_ids())
paths = [
EnvironmentSettings.root_path +
"test/tmp/datasplitter/split_{}".format(i)
for i in range(dataset.get_example_count())
]
for path in paths:
PathBuilder.build(path)
trains, tests = DataSplitter.run(
DataSplitterParams(dataset=dataset,
split_strategy=SplitType.LOOCV,
split_count=-1,
training_percentage=-1,
paths=paths))
self.assertTrue(isinstance(trains[0], RepertoireDataset))
self.assertTrue(isinstance(tests[0], RepertoireDataset))
self.assertEqual(len(trains[0].get_data()), 7)
self.assertEqual(len(tests[0].get_data()), 1)
self.assertEqual(8, len(trains))
self.assertEqual(8, len(tests))
paths = [
EnvironmentSettings.root_path +
"test/tmp/datasplitter/split_{}".format(i) for i in range(5)
]
for path in paths:
PathBuilder.build(path)
trains, tests = DataSplitter.run(
DataSplitterParams(dataset=dataset,
split_strategy=SplitType.K_FOLD,
split_count=5,
training_percentage=-1,
paths=paths))
self.assertTrue(isinstance(trains[0], RepertoireDataset))
self.assertTrue(isinstance(tests[0], RepertoireDataset))
self.assertEqual(len(trains[0].get_data()), 6)
self.assertEqual(len(tests[0].get_data()), 2)
self.assertEqual(5, len(trains))
self.assertEqual(5, len(tests))
shutil.rmtree(EnvironmentSettings.root_path + "test/tmp/datasplitter/")
def test_repertoire(self):
path = EnvironmentSettings.tmp_test_path + "sequencerepertoire/"
PathBuilder.build(path)
sequences = [
ReceptorSequence(amino_acid_sequence="AAA",
identifier="1",
metadata=SequenceMetadata(v_gene="V1",
cell_id="1",
chain=Chain.ALPHA,
custom_params={
"cmv": "no",
"coeliac": False
})),
ReceptorSequence(amino_acid_sequence="CCC",
identifier="2",
metadata=SequenceMetadata(j_gene="J1",
cell_id="1",
chain=Chain.BETA,
custom_params={
"cmv": "yes",
"coeliac": True
}))
]
obj = Repertoire.build_from_sequence_objects(sequences, path, {
"cmv": "yes",
'subject_id': "1"
})
self.assertTrue(os.path.isfile(obj.data_filename))
self.assertTrue(isinstance(obj, Repertoire))
self.assertTrue(
np.array_equal(np.array(["1", "2"]),
obj.get_sequence_identifiers()))
self.assertTrue(
np.array_equal(np.array(["AAA", "CCC"]), obj.get_sequence_aas()))
self.assertTrue(
np.array_equal(np.array(["V1", None]), obj.get_v_genes()))
self.assertTrue(
np.array_equal(np.array([None, "J1"]), obj.get_j_genes()))
self.assertTrue(
np.array_equal(np.array(["no", "yes"]), obj.get_attribute("cmv")))
self.assertTrue(
np.array_equal(np.array([False, True]),
obj.get_attribute("coeliac")))
self.assertEqual("yes", obj.metadata["cmv"])
self.assertEqual("1", obj.metadata["subject_id"])
rebuilt_sequences = obj.sequences
self.assertTrue(
all(
isinstance(seq, ReceptorSequence)
for seq in rebuilt_sequences))
self.assertEqual(2, len(rebuilt_sequences))
self.assertEqual("1", rebuilt_sequences[0].identifier)
self.assertEqual("2", rebuilt_sequences[1].identifier)
self.assertEqual("AAA", rebuilt_sequences[0].amino_acid_sequence)
self.assertEqual("yes",
rebuilt_sequences[1].metadata.custom_params["cmv"])
obj.free_memory()
self.assertTrue(key in obj.data for key in Repertoire.FIELDS)
self.assertTrue(obj.data[key] is None for key in Repertoire.FIELDS)
shutil.rmtree(path)
def store_repertoire(path, repertoire, sequences):
new_repertoire = Repertoire.build_from_sequence_objects(
sequences, path, repertoire.metadata)
return new_repertoire
