[docs]classGenericImport(DataImport):""" Imports data from any tabular file 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. This importer works similarly to other importers, but has no predefined default values for which fields are imported, and can therefore be tailored to import data from various different tabular files with headers. For ReceptorDatasets: this importer assumes the two receptor sequences appear on different lines in the file, and can be paired together by a common sequence identifier. If you instead want to import a ReceptorDataset from a tabular file that contains both receptor chains on one line, see :ref:`SingleLineReceptor` import **Specification arguments:** - path (str): For RepertoireDatasets, this is the path to a directory with files to import. For Sequence- or ReceptorDatasets this path may either be the path to the file to import, or the path to the folder locating one or multiple files with .tsv, .csv or .txt extensions. 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 labels, metadata_file is ignored, use label_columns instead. - label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file 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 a common identifier. This identifier should be mapped to the immuneML field 'sequence_identifiers' using the column_mapping. - 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 ChainPair enum. - 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 immuneML assumes the IMGT junction (including leading C and trailing Y/F amino acids) is used in the input file, and the first and last amino acids will be removed from the sequences to retrieve the IMGT CDR3 sequence. 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): Required for all datasets. A mapping where the keys are the column names in the input file, and the values correspond to the names in the AIRR format. A column mapping can look for example like this: .. indent with spaces .. code-block:: yaml file_column_amino_acids: cdr3_aa file_column_v_genes: v_call file_column_j_genes: j_call file_column_frequencies: duplicate_count - 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 Generic import, there is no default column_mapping_synonyms. - columns_to_load (list): Optional; specifies which columns to load from the input file. This may be useful if the input files contain many unused columns. If no value is specified, all columns are loaded. - separator (str): Required parameter. Column separator, for example "\\t" or ",". The default value is "\\t" **YAML specification:** .. indent with spaces .. code-block:: yaml definitions: datasets: my_generic_dataset: format: Generic 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 separator: "\\t" # column separator 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 amino acid sequence set region_type: IMGT_CDR3 # which column to check for illegal characters/empty strings etc column_mapping: # column mapping file: immuneML/AIRR column names file_column_amino_acids: junction_aa file_column_v_genes: v_call file_column_j_genes: j_call file_column_frequencies: duplicate_count file_column_antigen_specificity: antigen_specificity columns_to_load: # which subset of columns to load from the file - file_column_amino_acids - file_column_v_genes - file_column_j_genes - file_column_frequencies - file_column_antigen_specificity """
[docs]@staticmethoddefget_documentation():doc=str(GenericImport.__doc__)chain_pair_values=str([chain_pair.nameforchain_pairinChainPair])[1:-1].replace("'","`")region_type_values=str([region_type.nameforregion_typeinRegionType])[1:-1].replace("'","`")mapping={"Valid values for receptor_chains are the names of the 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}.",}doc=update_docs_per_mapping(doc,mapping)returndoc