Source code for immuneML.IO.dataset_import.IReceptorImport

import copy
import json
import shutil
import zipfile
from pathlib import Path

import airr
import pandas as pd

from immuneML.IO.dataset_import.AIRRImport import AIRRImport
from immuneML.IO.dataset_import.DataImport import DataImport
from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.data_model.receptor.ChainPair import ChainPair
from immuneML.data_model.receptor.RegionType import RegionType
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.util.ImportHelper import ImportHelper
from immuneML.util.PathBuilder import PathBuilder
from scripts.specification_util import update_docs_per_mapping

[docs] class IReceptorImport(DataImport): """ Imports AIRR datasets retrieved through the `iReceptor Gateway <>`_ into a Repertoire-, Sequence- or ReceptorDataset. The differences between this importer and the :ref:`AIRR` importer are: * This importer takes in a list of .zip files, which must contain one or more AIRR tsv files, and for each AIRR file, a corresponding metadata json file must be present. * This importer does not require a metadata csv file for RepertoireDataset import, it is generated automatically from the metadata json files. RepertoireDatasets should be used when making predictions per repertoire, such as predicting a disease state. SequenceDatasets or ReceptorDatasets should be used when predicting values for unpaired (single-chain) and paired immune receptors respectively, like antigen specificity. AIRR rearrangement schema can be found here: When importing a ReceptorDataset, the AIRR field cell_id is used to determine the chain pairs. Arguments: path (str): This is the path to a directory **with .zip files** retrieved from the iReceptor Gateway. These .zip files should include AIRR files (with .tsv extension) and corresponding metadata.json files with matching names (e.g., for my_dataset.tsv the corresponding metadata file is called my_dataset-metadata.json). The zip files must use the .zip extension. is_repertoire (bool): If True, this imports a RepertoireDataset. If False, it imports a SequenceDataset or ReceptorDataset. By default, is_repertoire is set to True. paired (str): Required for Sequence- or ReceptorDatasets. This parameter determines whether to import a SequenceDataset (paired = False) or a ReceptorDataset (paired = True). In a ReceptorDataset, two sequences with chain types specified by receptor_chains are paired together based on the identifier given in the AIRR column named 'cell_id'. receptor_chains (str): Required for ReceptorDatasets. Determines which pair of chains to import for each Receptor. Valid values for receptor_chains are the names of the :py:obj:`~immuneML.data_model.receptor.ChainPair.ChainPair` enum. If receptor_chains is not provided, the chain pair is automatically detected (only one chain pair type allowed per repertoire). import_productive (bool): Whether productive sequences (with value 'T' in column productive) should be included in the imported sequences. By default, import_productive is True. import_with_stop_codon (bool): Whether sequences with stop codons (with value 'T' in column stop_codon) should be included in the imported sequences. This only applies if column stop_codon is present. By default, import_with_stop_codon is False. import_out_of_frame (bool): Whether out of frame sequences (with value 'F' in column vj_in_frame) should be included in the imported sequences. This only applies if column vj_in_frame is present. By default, import_out_of_frame is False. import_illegal_characters (bool): Whether to import sequences that contain illegal characters, i.e., characters that do not appear in the sequence alphabet (amino acids including stop codon '*', or nucleotides). When set to false, filtering is only applied to the sequence type of interest (when running immuneML in amino acid mode, only entries with illegal characters in the amino acid sequence are removed). By default import_illegal_characters is False. import_empty_nt_sequences (bool): imports sequences which have an empty nucleotide sequence field; can be True or False. By default, import_empty_nt_sequences is set to True. import_empty_aa_sequences (bool): imports sequences which have an empty amino acid sequence field; can be True or False; for analysis on amino acid sequences, this parameter should be False (import only non-empty amino acid sequences). By default, import_empty_aa_sequences is set to False. region_type (str): Which part of the sequence to import. By default, this value is set to IMGT_CDR3. This means the first and last amino acids are removed from the CDR3 sequence, as AIRR uses the IMGT junction. Specifying any other value will result in importing the sequences as they are. Valid values for region_type are the names of the :py:obj:`~immuneML.data_model.receptor.RegionType.RegionType` enum. column_mapping (dict): A mapping from AIRR column names to immuneML's internal data representation. For AIRR, this is by default set to the values shown in YAML below. A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the AIRR file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the AIRR file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS. .. indent with spaces .. code-block:: yaml junction: sequences junction_aa: sequence_aas v_call: v_alleles j_call: j_alleles locus: chains duplicate_count: counts sequence_id: sequence_identifiers column_mapping_synonyms (dict): This is a column mapping that can be used if a column could have alternative names. The formatting is the same as column_mapping. If some columns specified in column_mapping are not found in the file, the columns specified in column_mapping_synonyms are instead attempted to be loaded. For AIRR format, there is no default column_mapping_synonyms. metadata_column_mapping (dict): Specifies metadata for Sequence- and ReceptorDatasets. This should specify a mapping similar to column_mapping where keys are AIRR column names and values are the names that are internally used in immuneML as metadata fields. These metadata fields can be used as prediction labels for Sequence- and ReceptorDatasets. For AIRR format, there is no default metadata_column_mapping. When importing a RepertoireDataset, the metadata is automatically extracted from the metadata json files. separator (str): Column separator, for AIRR this is by default "\\t". YAML specification: .. indent with spaces .. code-block:: yaml my_airr_dataset: format: IReceptor params: path: path/to/zipfiles/ is_repertoire: True # whether to import a RepertoireDataset metadata_column_mapping: # metadata column mapping AIRR: immuneML for Sequence- or ReceptorDatasetDataset airr_column_name1: metadata_label1 airr_column_name2: metadata_label2 import_productive: True # whether to include productive sequences in the dataset import_with_stop_codon: False # whether to include sequences with stop codon in the dataset import_out_of_frame: False # whether to include out of frame sequences in the dataset import_illegal_characters: False # remove sequences with illegal characters for the sequence_type being used import_empty_nt_sequences: True # keep sequences even if the `sequences` column is empty (provided that other fields are as specified here) import_empty_aa_sequences: False # remove all sequences with empty `sequence_aas` column # Optional fields with AIRR-specific defaults, only change when different behavior is required: separator: "\\t" # column separator region_type: IMGT_CDR3 # what part of the sequence to import column_mapping: # column mapping AIRR: immuneML junction: sequences junction_aa: sequence_aas v_call: v_alleles j_call: j_alleles locus: chains duplicate_count: counts sequence_id: sequence_identifiers """ REPERTOIRES_FOLDER = "repertoires/"
[docs] @staticmethod def import_dataset(params: dict, dataset_name: str) -> Dataset: if params["is_repertoire"]: dataset = IReceptorImport.import_repertoire_dataset(params, dataset_name) else: dataset = IReceptorImport.import_sequence_dataset(params, dataset_name) return dataset
[docs] @staticmethod def import_repertoire_dataset(params: dict, dataset_name: str) -> RepertoireDataset: base_result_path = params['result_path'] / "tmp_airr" metadata_file_path = base_result_path / "metadata.csv" IReceptorImport._create_airr_repertoiredataset(params['path'], base_result_path, metadata_file_path) airr_params = copy.deepcopy(params) airr_params["path"] = base_result_path airr_params["metadata_file"] = metadata_file_path dataset = ImportHelper.import_dataset(AIRRImport, airr_params, dataset_name) shutil.rmtree(base_result_path) return dataset
[docs] @staticmethod def import_sequence_dataset(params: dict, dataset_name: str) -> RepertoireDataset: base_result_path = params['result_path'] / "tmp_airr" unzipped_path = base_result_path / "tmp_unzipped" IReceptorImport._unzip_files(params['path'], unzipped_path, unzip_metadata=False) airr_params = copy.deepcopy(params) airr_params["path"] = unzipped_path dataset = ImportHelper.import_dataset(AIRRImport, airr_params, dataset_name) shutil.rmtree(unzipped_path) return dataset
@staticmethod def _create_airr_repertoiredataset(input_zips_path: Path, base_result_path: Path, metadata_file_path: Path): unzipped_path = base_result_path / "tmp_unzipped/" / IReceptorImport.REPERTOIRES_FOLDER) IReceptorImport._unzip_files(input_zips_path, unzipped_path) all_metadata_dfs = [] for airr_filename in unzipped_path.glob("*.tsv"): metadata_filename = unzipped_path / f"{airr_filename.stem}-metadata.json" sub_metadata_df = IReceptorImport._create_metadata_df(metadata_filename) files_written = IReceptorImport._split_airr_files(airr_filename, sub_metadata_df, base_result_path) sub_metadata_df = sub_metadata_df[files_written] all_metadata_dfs.append(sub_metadata_df) metadata_df = pd.concat(all_metadata_dfs, join="outer", ignore_index=True) metadata_df.fillna("NA", inplace=True) metadata_df.to_csv(metadata_file_path, index=False) shutil.rmtree(unzipped_path) @staticmethod def _unzip_files(path: Path, unzipped_path: Path, unzip_metadata=True) -> Dataset: for zip_filename in path.glob("*.zip"): with zipfile.ZipFile(zip_filename, "r") as zip_object: for file in zip_object.filelist: file.filename = f"{zip_filename.stem}_{file.filename}" if file.filename.endswith(".tsv") or (file.filename.endswith("-metadata.json") and unzip_metadata): zip_object.extract(file, path=unzipped_path) @staticmethod def _safe_get_field(dict, nested_fields): try: result = dict for field_name in nested_fields: result = result[field_name] except KeyError: result = None return result @staticmethod def _get_metadata_row(repertoire, sample, data_processing): repertoire_id = repertoire['repertoire_id'] sample_processing_id = sample['sample_processing_id'] data_processing_id = data_processing['data_processing_id'] filename = f"{IReceptorImport.REPERTOIRES_FOLDER}{repertoire_id}_{sample_processing_id}_{data_processing_id}.tsv".replace(" ", "-") subject_id = repertoire["subject"]["subject_id"] study_id = IReceptorImport._safe_get_field(repertoire, ["study", "study_id"]) species_label = IReceptorImport._safe_get_field(repertoire, ["subject", "species", "label"]) organism_label = IReceptorImport._safe_get_field(repertoire, ["subject", "organism", "label"]) sex = IReceptorImport._safe_get_field(repertoire, ["subject", "sex"]) age_min = IReceptorImport._safe_get_field(repertoire, ["subject", "age_min"]) age_max = IReceptorImport._safe_get_field(repertoire, ["subject", "age_max"]) age_event = IReceptorImport._safe_get_field(repertoire, ["subject", "age_event"]) ancestry_population = IReceptorImport._safe_get_field(repertoire, ["subject", "ancestry_population"]) ethnicity = IReceptorImport._safe_get_field(repertoire, ["subject", "ethnicity"]) race = IReceptorImport._safe_get_field(repertoire, ["subject", "race"]) strain_name = IReceptorImport._safe_get_field(repertoire, ["subject", "strain_name"]) tissue_label = IReceptorImport._safe_get_field(sample, ["tissue", "label"]) disease_state_sample = IReceptorImport._safe_get_field(sample, ["disease_state_sample"]) collection_time_point_relative = IReceptorImport._safe_get_field(sample, ["collection_time_point_relative"]) collection_time_point_reference = IReceptorImport._safe_get_field(sample, ["collection_time_point_reference"]) return (filename, subject_id, repertoire_id, sample_processing_id, data_processing_id, study_id, species_label, organism_label, sex, age_min, age_max, age_event, ancestry_population, ethnicity, race, strain_name, tissue_label, disease_state_sample, collection_time_point_relative, collection_time_point_reference) @staticmethod def _get_static_metadata_df(metadata_dict): identifiers = [IReceptorImport._get_metadata_row(repertoire, sample, data_processing) for repertoire in metadata_dict["Repertoire"] for sample in repertoire['sample'] for data_processing in repertoire['data_processing']] metadata_df = pd.DataFrame(identifiers, columns=["filename", "subject_id", "repertoire_id", "sample_processing_id", "data_processing_id", "study_id", "species_label", "organism_label", "sex", "age_min", "age_max", "age_event", "ancestry_population", "ethnicity", "race", "strain_name", "tissue_label", "disease_state_sample", "collection_time_point_relative", "collection_time_point_reference"]) metadata_df.dropna(axis=1, how="all", inplace=True) return metadata_df @staticmethod def _add_diagnosis_columns(metadata_df, metadata_dict): unique_diseases = set( [str(diagnosis["disease_diagnosis"]["label"]) for repertoire in metadata_dict["Repertoire"] for diagnosis in repertoire['subject']['diagnosis']]) id_sorted_repertoires = {repertoire["repertoire_id"]: repertoire for repertoire in metadata_dict["Repertoire"]} for disease_diagnosis_label in unique_diseases: corrected_label = disease_diagnosis_label.replace(" ", "_") metadata_df[corrected_label] = "NA" metadata_df[f"{corrected_label}_length"] = None metadata_df[f"{corrected_label}_stage"] = None metadata_df[f"{corrected_label}_immunogen"] = None for repertoire_id in metadata_df["repertoire_id"].unique(): label_sorted_diagnoses = {str(diagnosis["disease_diagnosis"]["label"]): diagnosis for diagnosis in id_sorted_repertoires[repertoire_id]["subject"]["diagnosis"]} for current_diagnosis_label in label_sorted_diagnoses.keys(): if current_diagnosis_label == disease_diagnosis_label: metadata_df.loc[metadata_df["repertoire_id"] == repertoire_id, corrected_label] = \ IReceptorImport._safe_get_field(label_sorted_diagnoses, [current_diagnosis_label, "study_group_description"]) metadata_df.loc[metadata_df["repertoire_id"] == repertoire_id, f"{corrected_label}_length"] = \ IReceptorImport._safe_get_field(label_sorted_diagnoses, [current_diagnosis_label, "disease_length"]) metadata_df.loc[metadata_df["repertoire_id"] == repertoire_id, f"{corrected_label}_stage"] = \ IReceptorImport._safe_get_field(label_sorted_diagnoses, [current_diagnosis_label, "disease_stage"]) metadata_df.loc[metadata_df["repertoire_id"] == repertoire_id, f"{corrected_label}_immunogen"] = \ IReceptorImport._safe_get_field(label_sorted_diagnoses, [current_diagnosis_label, "immunogen"]) metadata_df.dropna(axis=1, how="all", inplace=True) return metadata_df @staticmethod def _create_metadata_df(metadata_json): with open(metadata_json) as json_file: metadata_dict = json.load(json_file) metadata_df = IReceptorImport._get_static_metadata_df(metadata_dict) metadata_df = IReceptorImport._add_diagnosis_columns(metadata_df, metadata_dict) return metadata_df @staticmethod def _split_airr_files(airr_file: Path, metadata_df: pd.DataFrame, result_path: Path): airr_df = airr.load_rearrangement(airr_file) files_written = [] for filename, repertoire_id, sample_processing_id, data_processing_id in metadata_df[ ["filename", "repertoire_id", "sample_processing_id", "data_processing_id"]].itertuples(index=False): subset = airr_df[airr_df["repertoire_id"] == repertoire_id] if "sample_processing_id" in subset.columns and any(subset["sample_processing_id"].str.len() > 0): subset = subset[subset["sample_processing_id"] == str(sample_processing_id)] if "data_processing_id" in subset.columns and any(subset["data_processing_id"].str.len() > 0): subset = subset[subset["data_processing_id"] == str(data_processing_id)] if subset.empty: files_written.append(False) else: subset.to_csv(result_path / filename, index=False, sep="\t") files_written.append(True) return files_written
[docs] @staticmethod def get_documentation(): doc = str(IReceptorImport.__doc__) chain_pair_values = str([ for chain_pair in ChainPair])[1:-1].replace("'", "`") region_type_values = str([ for region_type in RegionType])[1:-1].replace("'", "`") repertoire_fields = list(Repertoire.FIELDS) repertoire_fields.remove("region_types") mapping = { "Valid values for receptor_chains are the names of the :py:obj:`~immuneML.data_model.receptor.ChainPair.ChainPair` enum.": f"Valid values are {chain_pair_values}.", "Valid values for region_type are the names of the :py:obj:`~immuneML.data_model.receptor.RegionType.RegionType` enum.": f"Valid values are {region_type_values}.", "Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS": f"Valid immuneML fields that can be specified here are {repertoire_fields}." } doc = update_docs_per_mapping(doc, mapping) return doc