def test(self):
is_installed = True
try:
from immuneML.ml_methods.DeepRC import DeepRC
from deeprc.deeprc_binary.architectures import DeepRC as DeepRCInternal
except Exception as e:
is_installed = False
if is_installed:
logging.warning("DeepRC test is temporarily excluded")
path = EnvironmentSettings.tmp_test_path / "deeprc_classifier"
data_path = path / "encoded_data"
result_path = path / "result"
PathBuilder.build(data_path)
PathBuilder.build(result_path)
encoded_data = self.make_encoded_data(data_path)
y = {"status": encoded_data.labels["status"]}
params = DefaultParamsLoader.load("ml_methods/", "DeepRC")
classifier = DeepRC(**params)
# Prepare 'dummy training' for classifier, to test other functionalities
classifier.result_path = path
classifier.pytorch_device = torch.device("cpu")
classifier.training_function = self.dummy_training_function
train_indices, val_indices = classifier._get_train_val_indices(10, y['status'])
self.assertEqual(len(train_indices) + len(val_indices), 10)
self.assertEqual(set(list(train_indices) + list(val_indices)), set(range(10)))
# test if 'fit' function saves models
classifier.fit(encoded_data, "status")
self.assertListEqual(classifier.get_classes(), ["A", "B"])
self.assertIsInstance(classifier.model, DeepRCInternal)
# Test storing and loading of models
self.assertFalse(classifier.check_if_exists(result_path))
classifier.store(result_path, feature_names=None)
self.assertTrue(classifier.check_if_exists(result_path))
second_classifier = DeepRC(**params)
second_classifier.load(result_path)
self.assertIsInstance(second_classifier.model, DeepRCInternal)
shutil.rmtree(path)
# test get package info
params = DefaultParamsLoader.load("ml_methods/", "DeepRC")
classifier = DeepRC(**params)
classifier.get_package_info()
else:
logging.warning("DeepRC is not installed, skipping test. To install DeepRC, install the requirements from requirements_DeepRC.txt.")
def test_load_repertoire(self):
"""Test dataset content with and without a header included in the input file"""
path = EnvironmentSettings.root_path / "test/tmp/io_igor_load/"
PathBuilder.build(path)
self.write_dummy_files(path, True)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "igor")
params["is_repertoire"] = True
params["result_path"] = path
params["path"] = path
params["metadata_file"] = path / "metadata.csv"
dataset = IGoRImport.import_dataset(params, "igor_repertoire_dataset")
self.assertEqual(2, dataset.get_example_count())
self.assertEqual(len(dataset.repertoires[0].sequences), 1)
self.assertEqual(len(dataset.repertoires[1].sequences), 1)
self.assertEqual(
dataset.repertoires[0].sequences[0].amino_acid_sequence,
"ARDRWSTPVLRYFDWWTPPYYYYMDV")
self.assertListEqual(list(dataset.repertoires[0].get_counts()), [1])
self.assertEqual(dataset.repertoires[0].get_chains(), None)
shutil.rmtree(path)
def test_import_repertoire_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/io_10xGenomics/"
PathBuilder.build(path)
self.create_dumy_dataset(path, add_metadata=True)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/",
"tenx_genomics")
params["is_repertoire"] = True
params["result_path"] = path
params["path"] = path
params["metadata_file"] = path / "metadata.csv"
dataset = TenxGenomicsImport.import_dataset(params,
"tenx_dataset_repertoire")
self.assertEqual(2, dataset.get_example_count())
self.assertEqual(len(dataset.repertoires[0].sequences), 2)
self.assertEqual(len(dataset.repertoires[1].sequences), 4)
self.assertEqual(
dataset.repertoires[0].sequences[0].amino_acid_sequence,
"ALSGTGGYKVV")
self.assertListEqual([Chain.ALPHA, Chain.BETA],
list(dataset.repertoires[0].get_chains()))
self.assertListEqual([2, 4], list(dataset.repertoires[0].get_counts()))
shutil.rmtree(path)
def test_import_receptor_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/io_10xGenomics/"
PathBuilder.build(path)
self.create_dumy_dataset(path, add_metadata=False)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/",
"tenx_genomics")
params["is_repertoire"] = False
params["paired"] = True
params["result_path"] = path
params["path"] = path
params["sequence_file_size"] = 1
params["receptor_chains"] = "TRA_TRB"
dataset = TenxGenomicsImport.import_dataset(params,
"tenx_dataset_receptor")
self.assertEqual(2, dataset.get_example_count())
self.assertEqual(2, len(dataset.get_filenames()))
data = dataset.get_data(1)
for receptor in data:
self.assertTrue(receptor.alpha.amino_acid_sequence in
["ALSGTGGYKVV", "AIVGNTGKLI"])
self.assertTrue(receptor.beta.amino_acid_sequence in
["ASSLYGGPEVF", "ASSFATNSDYT"])
shutil.rmtree(path)
def test_load_repertoire_with_stop_codon(self):
path = EnvironmentSettings.root_path / "test/tmp/io_igor_load/"
PathBuilder.build(path)
self.write_dummy_files(path, True)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "igor")
params["is_repertoire"] = True
params["result_path"] = path
params["path"] = path
params["import_with_stop_codon"] = True
params["metadata_file"] = path / "metadata.csv"
dataset_stop_codons = IGoRImport.import_dataset(
params, "igor_dataset_stop")
self.assertEqual(2, dataset_stop_codons.get_example_count())
self.assertEqual(len(dataset_stop_codons.repertoires[0].sequences), 2)
self.assertEqual(len(dataset_stop_codons.repertoires[1].sequences), 2)
self.assertEqual(
dataset_stop_codons.repertoires[0].sequences[0].
amino_acid_sequence, "ARVNRHIVVVTAIMTG*NWFDP")
shutil.rmtree(path)
def test_load_sequence_dataset(self):
"""Test dataset content with and without a header included in the input file"""
path = EnvironmentSettings.root_path / "test/tmp/io_igor_load/"
PathBuilder.build(path)
self.write_dummy_files(path, False)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "igor")
params["is_repertoire"] = False
params["paired"] = False
params["result_path"] = path
params["path"] = path
params["import_with_stop_codon"] = True
dataset = IGoRImport.import_dataset(params, "igor_seq_dataset")
seqs = [sequence for sequence in dataset.get_data()]
self.assertEqual(4, dataset.get_example_count())
self.assertListEqual(
sorted([
"GCGAGACGTGTCTAGGGAGGATATTGTAGTAGTACCAGCTGCTATGACGGGCGGTCCGGTAGTACTACTTTGACTAC",
"GCGAGAGGCTTCCATGGAACTACAGTAACTACGTTTGTAGGCTGTAGTACTACATGGACGTC",
"GCGAGAGTTAATCGGCATATTGTGGTGGTGACTGCTATTATGACCGGGTAAAACTGGTTCGACCCC",
"GCGAGAGATAGGTGGTCAACCCCAGTATTACGATATTTTGACTGGTGGACCCCGCCCTACTACTACTACATGGACGTC"
]), sorted([seq.nucleotide_sequence for seq in seqs]))
shutil.rmtree(path)
def test_alternative_repertoire_import(self):
path = EnvironmentSettings.root_path / "test/tmp/immunoseq_alternative/"
rep1text = """sample_name productive_frequency templates amino_acid rearrangement v_resolved d_resolved j_resolved
LivMet_45 0.014838454958215437 451 CASSLLGLGSEQYF CTGCTGTCGGCTGCTCCCTCCCAGACATCTGTGTACTTCTGTGCCAGCAGTTTACTCGGGTTAGGGAGCGAGCAGTACTTCGGGCCG TCRBV06 TCRBD02-01*02 TCRBJ02-07*01
LivMet_45 0.0106928999144568 325 CASSPGQGEGYEQYF CACGCCCTGCAGCCAGAAGACTCAGCCCTGTATCTCTGCGCCAGCAGCCCGGGACAGGGGGAGGGCTACGAGCAGTACTTCGGGCCG TCRBV04-01*01 TCRBD01-01*01 TCRBJ02-07*01
LivMet_45 0.0074356780943607296 226 CASSAGETQYF ACTCTGACGATCCAGCGCACAGAGCAGCGGGACTCGGCCATGTATCGCTGTGCCAGCAGCGCAGGCGAGACCCAGTACTTCGGGCCA TCRBV07-06*01 TCRBD01-01*01 TCRBJ02-05*01
LivMet_45 0.0072053694808185825 219 CASSGTGEKGEQYF ATCCGGTCCACAAAGCTGGAGGACTCAGCCATGTACTTCTGTGCCAGCAGTGGGACAGGGGAGAAGGGCGAGCAGTACTTCGGGCCG TCRBV02-01*01 TCRBD01-01*01 TCRBJ02-07*01
"""
PathBuilder.build(path)
with open(path / "rep1.tsv", "w") as file:
file.writelines(rep1text)
with open(path / "metadata.csv", "w") as file:
file.writelines("""filename,chain,subject_id
rep1.tsv,TRB,1234a""")
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/",
"ImmunoSEQRearrangement")
params["is_repertoire"] = True
params["result_path"] = path
params["metadata_file"] = path / "metadata.csv"
params["path"] = path
dataset = ImmunoSEQRearrangementImport.import_dataset(
params, "alternative")
self.assertEqual(1, dataset.get_example_count())
shutil.rmtree(path)
def _parse_split_config(self, instruction_key, instruction: dict, split_key: str, symbol_table: SymbolTable, settings_count: int) -> SplitConfig:
try:
default_params = DefaultParamsLoader.load("instructions/", SplitConfig.__name__)
report_config_input = self._prepare_report_config(instruction_key, instruction, split_key, symbol_table)
instruction[split_key] = {**default_params, **instruction[split_key]}
split_strategy = SplitType[instruction[split_key]["split_strategy"].upper()]
training_percentage = float(instruction[split_key]["training_percentage"]) if split_strategy == SplitType.RANDOM else -1
if split_strategy == SplitType.RANDOM and training_percentage == 1 and settings_count > 1:
raise ValueError(f"{TrainMLModelParser.__name__}: all data under {instruction_key}/{split_key} was specified to be used for "
f"training, but {settings_count} settings were specified for evaluation. Please define a test/validation set by "
f"reducing the training percentage (e.g., to 0.7) or use only one hyperparameter setting to run the analysis.")
return SplitConfig(split_strategy=split_strategy,
split_count=int(instruction[split_key]["split_count"]),
training_percentage=training_percentage,
reports=ReportConfig(**report_config_input),
manual_config=ManualSplitConfig(**instruction[split_key]["manual_config"]) if "manual_config" in instruction[split_key] else None,
leave_one_out_config=LeaveOneOutConfig(**instruction[split_key]["leave_one_out_config"])
if "leave_one_out_config" in instruction[split_key] else None)
except KeyError as key_error:
raise KeyError(f"{TrainMLModelParser.__name__}: parameter {key_error.args[0]} was not defined under {split_key}.")
def prepare_reference(reference_params: dict, location: str, paired: bool):
ParameterValidator.assert_keys(list(reference_params.keys()), ["format", "params"], location,
"reference")
seq_import_params = reference_params["params"] if "params" in reference_params else {}
assert os.path.isfile(seq_import_params["path"]), f"{location}: the file {seq_import_params['path']} does not exist. " \
f"Specify the correct path under reference."
if "is_repertoire" in seq_import_params:
assert seq_import_params["is_repertoire"] == False, f"{location}: is_repertoire must be False for SequenceImport"
else:
seq_import_params["is_repertoire"] = False
if "paired" in seq_import_params:
assert seq_import_params["paired"] == paired, f"{location}: paired must be {paired} for SequenceImport"
else:
seq_import_params["paired"] = paired
format_str = reference_params["format"]
import_class = ReflectionHandler.get_class_by_name("{}Import".format(format_str))
default_params = DefaultParamsLoader.load(EnvironmentSettings.default_params_path / "datasets",
DefaultParamsLoader.convert_to_snake_case(format_str))
params = {**default_params, **seq_import_params}
processed_params = DatasetImportParams.build_object(**params)
receptors = ImportHelper.import_items(import_class, reference_params["params"]["path"], processed_params)
return receptors
def _parse_ml_method(ml_method_id: str, ml_specification) -> tuple:
valid_class_values = ReflectionHandler.all_nonabstract_subclass_basic_names(
MLMethod, "", "ml_methods/")
if type(ml_specification) is str:
ml_specification = {ml_specification: {}}
ml_specification = {
**DefaultParamsLoader.load("ml_methods/", "MLMethod"),
**ml_specification
}
ml_specification_keys = list(ml_specification.keys())
ParameterValidator.assert_all_in_valid_list(
list(ml_specification_keys),
["model_selection_cv", "model_selection_n_folds"] +
valid_class_values, "MLParser", ml_method_id)
non_default_keys = [
key for key in ml_specification.keys()
if key not in ["model_selection_cv", "model_selection_n_folds"]
]
assert len(ml_specification_keys) == 3, f"MLParser: ML method {ml_method_id} was not correctly specified. Expected at least 1 key " \
f"(ML method name), got {len(ml_specification_keys) - 2} instead: " \
f"{str([key for key in non_default_keys])[1:-1]}."
ml_method_class_name = non_default_keys[0]
ml_method_class = ReflectionHandler.get_class_by_name(
ml_method_class_name, "ml_methods/")
ml_specification[ml_method_class_name] = {
**DefaultParamsLoader.load("ml_methods/",
ml_method_class_name,
log_if_missing=False),
**ml_specification[ml_method_class_name]
}
method, params = MLParser.create_method_instance(
ml_specification, ml_method_class, ml_method_id)
ml_specification[ml_method_class_name] = params
method.name = ml_method_id
return method, ml_specification
def parse_instruction(key: str, instruction: dict, symbol_table: SymbolTable, path) -> tuple:
ParameterValidator.assert_keys_present(list(instruction.keys()), ["type"], InstructionParser.__name__, key)
valid_instructions = [cls[:-6] for cls in ReflectionHandler.discover_classes_by_partial_name("Parser", "dsl/instruction_parsers/")]
ParameterValidator.assert_in_valid_list(instruction["type"], valid_instructions, "InstructionParser", "type")
default_params = DefaultParamsLoader.load("instructions/", instruction["type"])
instruction = {**default_params, **instruction}
parser = ReflectionHandler.get_class_by_name("{}Parser".format(instruction["type"]), "instruction_parsers/")()
instruction_object = parser.parse(key, instruction, symbol_table, path)
symbol_table.add(key, SymbolType.INSTRUCTION, instruction_object)
return instruction, symbol_table
def test_repertoire_import(self):
path = EnvironmentSettings.root_path / "test/tmp/adaptive/"
self.build_dummy_dataset(path, True)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/",
"ImmunoSEQRearrangement")
params["is_repertoire"] = True
params["result_path"] = path
params['import_empty_nt_sequences'] = False
params['import_empty_aa_sequences'] = True
params["metadata_file"] = path / "metadata.csv"
params["path"] = path
params["import_productive"] = True
params["import_with_stop_codon"] = True
params["import_out_of_frame"] = True
dataset_name = "adaptive_dataset_reps"
dataset = ImmunoSEQRearrangementImport.import_dataset(
params, dataset_name)
self.assertEqual(
dataset.repertoires[0].sequences[1].metadata.frame_type,
SequenceFrameType.IN)
self.assertListEqual(
list(dataset.repertoires[0].get_counts()),
[10, 1772, 1763, None, 566, 506, 398, 394, 363, 363])
self.assertListEqual(list(dataset.repertoires[0].get_chains()),
[Chain.BETA for i in range(10)])
self.assertEqual(2, dataset.get_example_count())
for index, rep in enumerate(dataset.get_data()):
if index == 0:
self.assertEqual("1234", rep.metadata["subject_id"])
self.assertEqual(10, len(rep.sequences))
self.assertEqual(10, rep.sequences[0].metadata.count)
self.assertEqual("TRBV29",
rep.sequences[0].metadata.v_subgroup)
else:
self.assertEqual("1234a", rep.metadata["subject_id"])
self.assertEqual(11, len(rep.sequences))
self.assertEqual(2, rep.sequences[-1].metadata.count)
dataset_file = path / f"{dataset_name}.{ImportHelper.DATASET_FORMAT}"
self.assertTrue(dataset_file.is_file())
shutil.rmtree(path)
def test_load(self):
params = {"a": 1, "b": True}
path = EnvironmentSettings.tmp_test_path / "defaultparamsloader/"
PathBuilder.build(path)
with open(path / "mixcr_params.yaml", "w") as file:
yaml.dump(params, file)
loaded = DefaultParamsLoader.load(path, "MiXCR")
self.assertTrue(all(key in loaded.keys() for key in params.keys()))
self.assertEqual(1, loaded["a"])
self.assertEqual(True, loaded["b"])
self.assertEqual(2, len(loaded.keys()))
shutil.rmtree(path)
def test_load_repertoire_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/mixcr/"
PathBuilder.build(path)
self.create_dummy_dataset(path, add_metadata=True)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "mixcr")
params["is_repertoire"] = True
params["result_path"] = path
params["path"] = path
params["metadata_file"] = path / "metadata.csv"
dataset = MiXCRImport.import_dataset(params,
"mixcr_repertoire_dataset")
self.assertEqual(2, dataset.get_example_count())
for index, repertoire in enumerate(dataset.get_data()):
self.assertTrue(
all(sequence.metadata.chain == Chain.ALPHA
for sequence in repertoire.sequences))
if index == 0:
self.assertEqual(9, len(repertoire.sequences))
self.assertTrue(repertoire.sequences[0].amino_acid_sequence
in ["ALVTDSWGKLQ",
"AVLETSGSRLT"]) # OSX/windows
self.assertTrue(repertoire.sequences[0].metadata.v_gene
in ["TRAV6", "TRAV21"]) # OSX/windows
self.assertListEqual([Chain.ALPHA for i in range(9)],
list(repertoire.get_chains()))
self.assertListEqual(
sorted([
956023, 90101, 69706, 56658, 55692, 43466, 42172,
41647, 19133
]), sorted(list(repertoire.get_counts())))
elif index == 1:
self.assertEqual(5, len(repertoire.sequences))
self.assertTrue(repertoire.sequences[0].nucleotide_sequence
in [
"GCTGTGCTGGAAACCAGTGGCTCTAGGTTGACC",
"GCTCTAGTAACTGACAGCTGGGGGAAATTGCAG"
]) # OSX/windows
shutil.rmtree(path)
def _prepare_params(dataset_specs: dict, result_path: Path,
dataset_name: str):
params = DefaultParamsLoader.load(ImportParser.keyword,
dataset_specs["format"])
if "params" in dataset_specs.keys():
params = {**params, **dataset_specs["params"]}
if "result_path" not in params or params["result_path"] is None:
params["result_path"] = Path(
result_path) / "datasets" / dataset_name
else:
params["result_path"] = Path(params["result_path"])
if "path" in params:
params["path"] = Path(params["path"])
if "metadata_file" in params:
params["metadata_file"] = Path(params["metadata_file"])
dataset_specs["params"] = params
return params
def test_sequence_import(self):
path = EnvironmentSettings.root_path / "test/tmp/adaptive/"
self.build_dummy_dataset(path, False)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/",
"ImmunoSEQRearrangement")
params["is_repertoire"] = False
params["paired"] = False
params["result_path"] = path
params["path"] = path
params["import_productive"] = True
params["import_with_stop_codon"] = True
params["import_out_of_frame"] = True
params["import_empty_nt_sequences"] = False
params["import_empty_aa_sequences"] = True
dataset_name = "adaptive_dataset_seqs"
dataset = ImmunoSEQRearrangementImport.import_dataset(
params, dataset_name)
self.assertEqual(21, dataset.get_example_count())
seqs = [sequence for sequence in dataset.get_data()]
self.assertTrue(seqs[0].amino_acid_sequence
in ["ASSLPGTNTGELF", "SVEESYEQY"]) # OSX/windows
self.assertTrue(seqs[0].nucleotide_sequence in [
"GCCAGCAGCTTACCGGGGACGAACACCGGGGAGCTGTTT",
'AGCGTTGAAGAATCCTACGAGCAGTAC'
]) # OSX/windows
self.assertEqual("IN", seqs[0].metadata.frame_type.name)
self.assertTrue(seqs[0].metadata.v_gene in ['TRBV7-9',
'TRBV29-1']) # OSX/windows
self.assertTrue(seqs[0].metadata.j_gene in ['TRBJ2-2',
'TRBJ2-7']) # OSX/windows
dataset_file = path / f"{dataset_name}.{ImportHelper.DATASET_FORMAT}"
self.assertTrue(dataset_file.is_file())
shutil.rmtree(path)
def test_encode(self):
file_content = """complex.id Gene CDR3 V J Species MHC A MHC B MHC class Epitope Epitope gene Epitope species Reference Method Meta CDR3fix Score
3050 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15760 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3050 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15760 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
3051 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15761 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3051 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15761 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
"""
path = PathBuilder.build(EnvironmentSettings.root_path /
"test/tmp/trcdist_encoder/")
with open(path / "receptors.tsv", "w") as file:
file.writelines(file_content)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "vdjdb")
params["is_repertoire"] = False
params["paired"] = True
params["result_path"] = path
params["path"] = path
params["sequence_file_size"] = 1
params["receptor_chains"] = "TRA_TRB"
params['organism'] = 'human'
dataset = VDJdbImport.import_dataset(params, "vdjdb_dataset")
encoder = TCRdistEncoder.build_object(dataset, **{"cores": 2})
encoded_dataset = encoder.encode(
dataset,
EncoderParams(path / "result/",
LabelConfiguration([Label("epitope")])))
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0]
== encoded_dataset.encoded_data.examples.shape[1]
and encoded_dataset.encoded_data.examples.shape[0]
== dataset.get_example_count())
shutil.rmtree(path)
def test_import_sequence_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/immunoseq/"
self.create_dummy_dataset(path, False)
params = DefaultParamsLoader.load(EnvironmentSettings.default_params_path / "datasets/", "ImmunoSEQSample")
params["is_repertoire"] = False
params["paired"] = False
params["result_path"] = path
params["path"] = path
dataset = ImmunoSEQSampleImport.import_dataset(params, "immunoseq_dataset")
seqs = [sequence for sequence in dataset.get_data()]
self.assertEqual(seqs[0].amino_acid_sequence, "ATSDQLNRWGTGELF")
self.assertEqual(seqs[1].amino_acid_sequence, "ASKDGDTGELF")
self.assertEqual(seqs[2].amino_acid_sequence, "ASSGEGQGVFGGTEAF")
self.assertEqual(seqs[3].amino_acid_sequence, "ASSEEVGGNQPQH")
shutil.rmtree(path)
def test_load_sequence_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/mixcr/"
PathBuilder.build(path)
self.create_dummy_dataset(path, add_metadata=False)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path / "datasets/", "mixcr")
params["is_repertoire"] = False
params["paired"] = False
params["result_path"] = path
params["path"] = path
dataset = MiXCRImport.import_dataset(params,
"mixcr_repertoire_dataset")
seqs = [sequence for sequence in dataset.get_data()]
self.assertTrue(seqs[0].amino_acid_sequence
in ["AVLETSGSRLT", "ALVTDSWGKLQ"]) # OSX/windows
self.assertTrue(seqs[0].metadata.v_gene in ["TRAV21",
"TRAV6"]) # OSX/windows
shutil.rmtree(path)
def test_import_repertoire_dataset(self):
path = EnvironmentSettings.root_path / "test/tmp/immunoseq/"
self.create_dummy_dataset(path, True)
params = DefaultParamsLoader.load(EnvironmentSettings.default_params_path / "datasets/", "ImmunoSEQSample")
params["is_repertoire"] = True
params["result_path"] = path
params["metadata_file"] = path / "metadata.csv"
params["path"] = path
dataset = ImmunoSEQSampleImport.import_dataset(params, "immunoseq_dataset")
self.assertEqual(1, dataset.get_example_count())
for index, rep in enumerate(dataset.get_data()):
self.assertEqual("1234a", rep.metadata["subject_id"])
self.assertEqual(18, len(rep.sequences))
self.assertEqual("ATSDQLNRWGTGELF", rep.sequences[0].get_sequence())
self.assertEqual("TRBV25-1", rep.sequences[2].metadata.v_gene)
self.assertListEqual([38, 48, 37, 53, 28, 16, 72, 14, 26, 13, 8, 16, 8, 28, 7, 1, 9, 1], list(rep.get_counts()))
self.assertListEqual([Chain.BETA for i in range(18)], list(rep.get_chains()))
shutil.rmtree(path)
def _get_implanting_strategy(key: str,
signal: dict) -> SignalImplantingStrategy:
valid_strategies = [
cls[:-10]
for cls in ReflectionHandler.discover_classes_by_partial_name(
"Implanting", "simulation/signal_implanting_strategy/")
]
ParameterValidator.assert_in_valid_list(signal["implanting"],
valid_strategies,
"SignalParser", key)
defaults = DefaultParamsLoader.load(
"signal_implanting_strategy/", f"{signal['implanting']}Implanting")
signal = {**defaults, **signal}
ParameterValidator.assert_keys_present(
list(signal.keys()),
["motifs", "implanting", "sequence_position_weights"],
SignalParser.__name__, key)
implanting_comp = None
if 'implanting_computation' in signal:
implanting_comp = signal['implanting_computation'].lower()
ParameterValidator.assert_in_valid_list(
implanting_comp,
[el.name.lower() for el in ImplantingComputation],
SignalParser.__name__, 'implanting_computation')
implanting_comp = ImplantingComputation[implanting_comp.upper()]
implanting_strategy = ReflectionHandler.get_class_by_name(
f"{signal['implanting']}Implanting")(
GappedMotifImplanting(), signal["sequence_position_weights"],
implanting_comp)
return implanting_strategy
def get_all_params(specs, class_path, short_class_name, key: str = None):
default_params = DefaultParamsLoader.load(class_path, short_class_name)
specified_params = ObjectParser.get_params(specs, short_class_name)
params = {**default_params, **specified_params, "name": key}
return params