Source code for immuneML.IO.dataset_import.TenxGenomicsImport

import pandas as pd

from immuneML.IO.dataset_import.DataImport import DataImport
from immuneML.IO.dataset_import.DatasetImportParams import DatasetImportParams
from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.data_model.receptor.ChainPair import ChainPair
from immuneML.data_model.receptor.RegionType import RegionType
from immuneML.data_model.receptor.receptor_sequence.SequenceFrameType import SequenceFrameType
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.util.ImportHelper import ImportHelper
from scripts.specification_util import update_docs_per_mapping

[docs]class TenxGenomicsImport(DataImport): """ Imports data from the 10x Genomics Cell Ranger analysis pipeline into a Repertoire-, Sequence- or ReceptorDataset. 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. The files that should be used as input are named 'Clonotype consensus annotations (CSV)', as described here: Note: by default the 10xGenomics field 'umis' is used to define the immuneML field counts. If you want to use the 10x Genomics field reads instead, this can be changed in the column_mapping (set reads: counts). Furthermore, the 10xGenomics field clonotype_id is used for the immuneML field cell_id. Arguments: path (str): This is the path to a directory with 10xGenomics files to import. By default path is set to the current working directory. 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. metadata_file (str): Required for RepertoireDatasets. This parameter specifies the path to the metadata file. This is a csv file with columns filename, subject_id and arbitrary other columns which can be used as labels in instructions. For setting Sequence- or ReceptorDataset metadata, metadata_file is ignored, see metadata_column_mapping instead. 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 10xGenomics column named 'clonotype_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_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 10xGenomics uses IMGT junction as CDR3. 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 10xGenomics column names to immuneML's internal data representation. For 10xGenomics, this is by default set to: .. indent with spaces .. code-block:: yaml cdr3: sequence_aas cdr3_nt: sequences v_gene: v_genes j_gene: j_genes umis: counts chain: chains clonotype_id: cell_ids consensus_id: sequence_identifiers A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the 10xGenomics file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS 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 10xGenomics 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 10xGenomics 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 10xGenomics format, there is no default metadata_column_mapping. For setting RepertoireDataset metadata, metadata_column_mapping is ignored, see metadata_file instead. separator (str): Column separator, for 10xGenomics this is by default ",". YAML specification: .. indent with spaces .. code-block:: yaml my_10x_dataset: format: 10xGenomics params: path: path/to/files/ is_repertoire: True # whether to import a RepertoireDataset metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset paired: False # whether to import SequenceDataset (False) or ReceptorDataset (True) when is_repertoire = False receptor_chains: TRA_TRB # what chain pair to import for a ReceptorDataset metadata_column_mapping: # metadata column mapping 10xGenomics: immuneML for SequenceDataset tenx_column_name1: metadata_label1 tenx_column_name2: metadata_label2 import_illegal_characters: False # remove sequences with illegal characters for the sequence_type being used import_empty_nt_sequences: True # keep sequences even though the nucleotide sequence might be empty import_empty_aa_sequences: False # filter out sequences if they don't have sequence_aa set # Optional fields with 10xGenomics-specific defaults, only change when different behavior is required: separator: "," # column separator region_type: IMGT_CDR3 # what part of the sequence to import column_mapping: # column mapping 10xGenomics: immuneML cdr3: sequence_aas cdr3_nt: sequences v_gene: v_genes j_gene: j_genes umis: counts chain: chains clonotype_id: cell_ids consensus_id: sequence_identifiers """
[docs] @staticmethod def import_dataset(params: dict, dataset_name: str) -> Dataset: return ImportHelper.import_dataset(TenxGenomicsImport, params, dataset_name)
[docs] @staticmethod def preprocess_dataframe(df: pd.DataFrame, params: DatasetImportParams): df["frame_types"] = None df.loc[df["productive"].eq("True"), "frame_types"] = allowed_productive_values = [] if params.import_productive: allowed_productive_values.append("True") if params.import_unproductive: allowed_productive_values.append("False") df = df[df.productive.isin(allowed_productive_values)] ImportHelper.junction_to_cdr3(df, params.region_type) ImportHelper.drop_empty_sequences(df, params.import_empty_aa_sequences, params.import_empty_nt_sequences) ImportHelper.drop_illegal_character_sequences(df, params.import_illegal_characters) ImportHelper.update_gene_info(df) ImportHelper.load_chains(df) return df
[docs] @staticmethod def import_receptors(df, params): df["receptor_identifiers"] = df["cell_ids"] return ImportHelper.import_receptors(df, params)
[docs] @staticmethod def get_documentation(): doc = str(TenxGenomicsImport.__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