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 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_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.assertEqual(
"GCGAGACGTGTCTAGGGAGGATATTGTAGTAGTACCAGCTGCTATGACGGGCGGTCCGGTAGTACTACTTTGACTAC",
seqs[0].nucleotide_sequence)
self.assertEqual(
"GCGAGAGGCTTCCATGGAACTACAGTAACTACGTTTGTAGGCTGTAGTACTACATGGACGTC",
seqs[1].nucleotide_sequence)
self.assertEqual(
"GCGAGAGTTAATCGGCATATTGTGGTGGTGACTGCTATTATGACCGGGTAAAACTGGTTCGACCCC",
seqs[2].nucleotide_sequence)
self.assertEqual(
"GCGAGAGATAGGTGGTCAACCCCAGTATTACGATATTTTGACTGGTGGACCCCGCCCTACTACTACTACATGGACGTC",
seqs[3].nucleotide_sequence)
shutil.rmtree(path)
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_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_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 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 _prepare_params(dataset_specs: dict, result_path: str, 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"] = f"{result_path}datasets/{dataset_name}/"
dataset_specs["params"] = params
return params
def test_load_repertoire_dataset(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
"""
path = EnvironmentSettings.root_path + "test/tmp/iovdjdb2/"
PathBuilder.build(path)
number_of_repertoires = 5
for i in range(number_of_repertoires):
with open(path + "receptors_{}.tsv".format(i + 1), "w") as file:
file.writelines(file_content)
metadata = {
"filename": [
"receptors_{}.tsv".format(i + 1)
for i in range(number_of_repertoires)
],
"label1": [i % 2 for i in range(number_of_repertoires)]
}
pd.DataFrame(metadata).to_csv(path + "metadata.csv")
default_params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path + "datasets/", "vdjdb")
dataset = VDJdbImport.import_dataset(
{
"is_repertoire": True,
"result_path": path,
"metadata_file": path + "metadata.csv",
"path": path,
"import_empty_nt_sequences": True,
"import_empty_aa_sequences": False,
"import_illegal_characters": False,
"column_mapping": default_params["column_mapping"],
"separator": "\t",
"region_type": "IMGT_CDR3"
}, "vdjdb_rep_dataset")
self.assertEqual(number_of_repertoires, dataset.get_example_count())
self.assertEqual(number_of_repertoires, len(dataset.get_data()))
for repertoire in dataset.get_data(2):
self.assertTrue(repertoire.metadata["label1"] in {0, 1})
self.assertEqual(4, len(repertoire.sequences))
self.assertListEqual(
[Chain.BETA, Chain.BETA, Chain.ALPHA, Chain.ALPHA],
list(repertoire.get_chains()))
self.assertEqual(None, repertoire.get_counts())
shutil.rmtree(path)
def test(self):
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)
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_for_label("status"),
["A", "B"])
self.assertIsInstance(classifier.models, dict)
self.assertListEqual(list(classifier.models.keys()), ["status"])
for model in classifier.models.values():
self.assertIsInstance(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.models, dict)
self.assertListEqual(list(second_classifier.models.keys()), ["status"])
for model in second_classifier.models.values():
self.assertIsInstance(model, DeepRCInternal)
shutil.rmtree(path)
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 "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"]
if format_str == "IRIS": # todo refactor this when refactoring IRISSequenceImport
receptors = IRISSequenceImport.import_items(**seq_import_params)
else:
import_class = ReflectionHandler.get_class_by_name(
"{}Import".format(format_str))
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path + "datasets/",
DefaultParamsLoader.convert_to_snake_case(format_str))
for key, value in seq_import_params.items():
params[key] = value
params["paired"] = paired
processed_params = DatasetImportParams.build_object(**params)
receptors = ImportHelper.import_items(
import_class, reference_params["params"]["path"],
processed_params)
return receptors
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 = f"{path}{dataset_name}.{ImportHelper.DATASET_FORMAT}"
self.assertTrue(os.path.isfile(dataset_file))
shutil.rmtree(path)
def make_reports_docs(path):
filename = "reports.rst"
open(path + filename, "w").close()
for report_type_class in [DataReport, EncodingReport, MLReport, TrainMLModelReport, MultiDatasetReport]:
with open(path + filename, "a") as file:
doc_format = DocumentationFormat(cls=report_type_class,
cls_name=f"**{report_type_class.get_title()}**",
level_heading=DocumentationFormat.LEVELS[1])
write_class_docs(doc_format, file)
subdir = DefaultParamsLoader.convert_to_snake_case(report_type_class.__name__) + "s"
classes = ReflectionHandler.all_nonabstract_subclasses(report_type_class, "", f"reports/{subdir}/")
make_docs(path, classes, filename, "", "a")
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 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.assertEqual("ALVTDSWGKLQ",
repertoire.sequences[0].amino_acid_sequence)
self.assertEqual("ALRITQGGSEKLV",
repertoire.sequences[1].amino_acid_sequence)
self.assertEqual("TRAV6",
repertoire.sequences[0].metadata.v_gene)
self.assertEqual("TRAV16",
repertoire.sequences[1].metadata.v_gene)
self.assertListEqual([Chain.ALPHA for i in range(9)],
list(repertoire.get_chains()))
self.assertListEqual([
956023, 90101, 69706, 56658, 55692, 43466, 42172, 41647,
19133
], list(repertoire.get_counts()))
elif index == 1:
self.assertEqual(5, len(repertoire.sequences))
self.assertEqual("GCTGTGCTGGAAACCAGTGGCTCTAGGTTGACC",
repertoire.sequences[0].nucleotide_sequence)
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(f"{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_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 _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 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.assertEqual("AVLETSGSRLT", seqs[0].amino_acid_sequence)
self.assertEqual("AVNDAGNMLT", seqs[1].amino_acid_sequence)
self.assertEqual("TRAV21", seqs[0].metadata.v_gene)
self.assertEqual("TRAV12-2", seqs[1].metadata.v_gene)
shutil.rmtree(path)
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.assertEqual("ASSLPGTNTGELF", seqs[0].amino_acid_sequence)
self.assertEqual("IN", seqs[0].metadata.frame_type.name)
self.assertEqual('TRBV7-9', seqs[0].metadata.v_gene)
self.assertEqual('TRBJ2-2', seqs[0].metadata.j_gene)
self.assertEqual('GCCAGCAGCTTACCGGGGACGAACACCGGGGAGCTGTTT',
seqs[0].nucleotide_sequence)
dataset_file = f"{path}{dataset_name}.{ImportHelper.DATASET_FORMAT}"
self.assertTrue(os.path.isfile(dataset_file))
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 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
def test_load_galaxy_bordercases(self):
# This test is here because in the Galaxy interface, when importing data from VDJdb:
# - receptors might be incomplete (one of two genes only)
# - V and J genes might be missing
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 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
4000 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
3050 TRA CAAIYESRGSTLGRLYF TRAV13-1*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": "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 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 = EnvironmentSettings.root_path + "test/tmp/iovdjdb/"
PathBuilder.build(path)
with open(path + "receptors.tsv", "w") as file:
file.writelines(file_content)
default_params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path + "datasets/", "vdjdb")
dataset = VDJdbImport.import_dataset(
{
"is_repertoire":
False,
"result_path":
path,
"paired":
True,
"path":
path,
"sequence_file_size":
1,
"region_type":
"IMGT_CDR3",
"separator":
"\t",
"receptor_chains":
"TRA_TRB",
"column_mapping":
default_params["column_mapping"],
"import_empty_nt_sequences":
True,
"import_empty_aa_sequences":
False,
"import_illegal_characters":
False,
"metadata_column_mapping":
default_params["metadata_column_mapping"]
}, "vdjdb_rec_dataset")
self.assertEqual(2, dataset.get_example_count())
self.assertEqual(2, len(dataset.get_filenames()))
for receptor in dataset.get_data(2):
self.assertTrue(receptor.alpha.amino_acid_sequence in
["AAIYESRGSTLGRLY", "ALRLNNQGGKLI"])
self.assertTrue(
receptor.alpha.get_attribute("v_gene") in ["TRAV13-1", None])
self.assertTrue(receptor.alpha.get_attribute("j_gene") in [None])
self.assertTrue(
receptor.beta.get_attribute("v_gene") in ["TRBV5-4", None])
self.assertTrue(
receptor.beta.get_attribute("j_gene") in
["TRBJ2-1", "TRBJ2-6"])
self.assertTrue(
receptor.metadata["epitope_species"] in ["EBV", "CMV"])
self.assertTrue(
receptor.metadata["epitope"] in ["AVFDRKSDAK", "KLGGALQAK"])
self.assertTrue(
receptor.metadata["epitope_gene"] in ["EBNA4", "IE1"])
shutil.rmtree(path)