immuneML.IO.dataset_import package

Submodules

immuneML.IO.dataset_import.AIRRImport module

class immuneML.IO.dataset_import.AIRRImport.AIRRImport

Bases: DataImport

Imports data in AIRR format 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 AIRR .tsv format is explained here: https://docs.airr-community.org/en/stable/datarep/format.html And the AIRR rearrangement schema can be found here: https://docs.airr-community.org/en/stable/datarep/rearrangements.html

When importing a ReceptorDataset, the AIRR field cell_id is used to determine the chain pairs.

Parameters:

• path (str) – For RepertoireDatasets, this is the path to a directory with AIRR 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. Only the AIRR files included under the column ‘filename’ are imported into the RepertoireDataset. For setting SequenceDataset 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 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 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 RegionType enum.

• column_mapping (dict) –

  A mapping from AIRR column names to immuneML’s internal data representation. 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. For AIRR, this is by default set to:

  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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For AIRR format, there is no default metadata_column_mapping.

• separator (str) – Column separator, for AIRR this is by default “t”.

YAML specification:

my_airr_dataset:
    format: AIRR
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset
        metadata_file: path/to/metadata.csv # metadata file for 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

static alternative_load_func(filename, params)

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → Dataset

static import_receptors(df, params)

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

Function for preprocessing data from a dataframe containing AIRR data, such that:

• productive sequences, sequences with stop codons or out of frame sequences are filtered according to specification

• if RegionType is CDR3, the leading C and trailing W are removed from the sequence to match the CDR3 definition

• if no chain column was specified, the chain is extracted from the v gene name

• the allele information is removed from the V and J genes

immuneML.IO.dataset_import.DataImport module

class immuneML.IO.dataset_import.DataImport.DataImport

Bases: object

abstract static import_dataset(params, dataset_name: str) → Dataset

immuneML.IO.dataset_import.DatasetImportParams module

class immuneML.IO.dataset_import.DatasetImportParams.DatasetImportParams(path: pathlib.Path = None, is_repertoire: bool = None, metadata_file: pathlib.Path = None, paired: bool = None, receptor_chains: immuneML.data_model.receptor.ChainPair.ChainPair = None, result_path: pathlib.Path = None, columns_to_load: list = None, separator: str = None, column_mapping: dict = None, column_mapping_synonyms: dict = None, region_type: immuneML.data_model.receptor.RegionType.RegionType = None, import_productive: bool = None, import_unproductive: bool = None, import_with_stop_codon: bool = None, import_out_of_frame: bool = None, import_illegal_characters: bool = None, metadata_column_mapping: dict = None, number_of_processes: int = 1, sequence_file_size: int = 50000, organism: str = None, import_empty_nt_sequences: bool = None, import_empty_aa_sequences: bool = None)

Bases: object

classmethod build_object(path: Path = None, metadata_file: Path = None, result_path: Path = None, region_type: str = None, receptor_chains: str = None, **kwargs)

column_mapping: dict = None

column_mapping_synonyms: dict = None

columns_to_load: list = None

import_empty_aa_sequences: bool = None

import_empty_nt_sequences: bool = None

import_illegal_characters: bool = None

import_out_of_frame: bool = None

import_productive: bool = None

import_unproductive: bool = None

import_with_stop_codon: bool = None

is_repertoire: bool = None

metadata_column_mapping: dict = None

metadata_file: Path = None

number_of_processes: int = 1

organism: str = None

paired: bool = None

path: Path = None

receptor_chains: ChainPair = None

region_type: RegionType = None

result_path: Path = None

separator: str = None

sequence_file_size: int = 50000

immuneML.IO.dataset_import.GenericImport module

class immuneML.IO.dataset_import.GenericImport.GenericImport

Bases: 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 SingleLineReceptor import

Parameters:

• 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 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 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 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 used in immuneML’s internal data representation. Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS. At least sequences (nucleotide) or sequence_aas (amino acids) must be specified, but all other fields are optional. A column mapping can look for example like this:

  file_column_amino_acids: sequence_aas
  file_column_v_genes: v_genes
  file_column_j_genes: j_genes
  file_column_frequencies: counts
  

• 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.

• metadata_column_mapping (dict) –

  Specifies metadata for Sequence- and ReceptorDatasets. This should specify a mapping similar to column_mapping where keys are file 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. There is no default metadata_column_mapping.

  file_column_antigen_specificity: antigen_specificity
  

• 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:

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 sequence_aa set
        region_type: IMGT_CDR3 # what part of the sequence to import
        column_mapping: # column mapping file: immuneML
            file_column_amino_acids: sequence_aas
            file_column_v_genes: v_genes
            file_column_j_genes: j_genes
            file_column_frequencies: counts
        metadata_column_mapping: # metadata column mapping file: immuneML
            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

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → <module 'immuneML.data_model.dataset.Dataset' from '/Users/milenpa/PycharmProjects/BMIimmuneML/immuneML/data_model/dataset/Dataset.py'>

static import_receptors(df, params)

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

immuneML.IO.dataset_import.IGoRImport module

class immuneML.IO.dataset_import.IGoRImport.IGoRImport

Bases: DataImport

Imports data generated by IGoR simulations into a Repertoire-, or SequenceDataset. RepertoireDatasets should be used when making predictions per repertoire, such as predicting a disease state. SequenceDatasets should be used when predicting values for unpaired (single-chain) immune receptors, like antigen specificity.

Note that you should run IGoR with the –CDR3 option specified, this tool imports the generated CDR3 files. Sequences with missing anchors are not imported, meaning only sequences with value ‘1’ in the anchors_found column are imported. Nucleotide sequences are automatically translated to amino acid sequences.

Reference: Quentin Marcou, Thierry Mora, Aleksandra M. Walczak ‘High-throughput immune repertoire analysis with IGoR’. Nature Communications, (2018) doi.org/10.1038/s41467-018-02832-w.

Parameters:

• path (str) – For RepertoireDatasets, this is the path to a directory with IGoR 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. 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. Only the IGoR files included under the column ‘filename’ are imported into the RepertoireDataset. For setting SequenceDataset metadata, metadata_file is ignored, see metadata_column_mapping instead.

• import_with_stop_codon (bool) – Whether sequences with stop codons should be included in the imported sequences. By default, import_with_stop_codon is False.

• import_out_of_frame (bool) – Whether out of frame sequences (with value ‘0’ in column is_inframe) should be included in the imported sequences. 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.

• 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 IGoR 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 RegionType enum.

• column_mapping (dict) –

  A mapping from IGoR column names to immuneML’s internal data representation. A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the IGoR file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS. For IGoR, this is by default set to:

  nt_CDR3: sequences
  seq_index: 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 IGoR 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 IGoR 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For IGoR format, there is no default metadata_column_mapping.

• separator (str) – Column separator, for IGoR this is by default “,”.

YAML specification:

my_igor_dataset:
    format: IGoR
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset (True) or a SequenceDataset (False)
        metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset
        metadata_column_mapping: # metadata column mapping IGoR: immuneML for SequenceDataset
            igor_column_name1: metadata_label1
            igor_column_name2: metadata_label2
        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 though the nucleotide sequence might be empty
        # Optional fields with IGoR-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 IGoR: immuneML
            nt_CDR3: sequences
            seq_index: sequence_identifiers

CODON_TABLE = {'AAA': 'K', 'AAC': 'N', 'AAG': 'K', 'AAT': 'N', 'ACA': 'T', 'ACC': 'T', 'ACG': 'T', 'ACT': 'T', 'AGA': 'R', 'AGC': 'S', 'AGG': 'R', 'AGT': 'S', 'ATA': 'I', 'ATC': 'I', 'ATG': 'M', 'ATT': 'I', 'CAA': 'Q', 'CAC': 'H', 'CAG': 'Q', 'CAT': 'H', 'CCA': 'P', 'CCC': 'P', 'CCG': 'P', 'CCT': 'P', 'CGA': 'R', 'CGC': 'R', 'CGG': 'R', 'CGT': 'R', 'CTA': 'L', 'CTC': 'L', 'CTG': 'L', 'CTT': 'L', 'GAA': 'E', 'GAC': 'D', 'GAG': 'E', 'GAT': 'D', 'GCA': 'A', 'GCC': 'A', 'GCG': 'A', 'GCT': 'A', 'GGA': 'G', 'GGC': 'G', 'GGG': 'G', 'GGT': 'G', 'GTA': 'V', 'GTC': 'V', 'GTG': 'V', 'GTT': 'V', 'TAA': '*', 'TAC': 'Y', 'TAG': '*', 'TAT': 'Y', 'TCA': 'S', 'TCC': 'S', 'TCG': 'S', 'TCT': 'S', 'TGA': '*', 'TGC': 'C', 'TGG': 'W', 'TGT': 'C', 'TTA': 'L', 'TTC': 'F', 'TTG': 'L', 'TTT': 'F'}

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → <module 'immuneML.data_model.dataset.Dataset' from '/Users/milenpa/PycharmProjects/BMIimmuneML/immuneML/data_model/dataset/Dataset.py'>

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

static translate_sequence(nt_seq)

Code inspired by: https://github.com/prestevez/dna2proteins/blob/master/dna2proteins.py

immuneML.IO.dataset_import.IReceptorImport module

class immuneML.IO.dataset_import.IReceptorImport.IReceptorImport

Bases: DataImport

Imports AIRR datasets retrieved through the iReceptor Gateway into a Repertoire-, Sequence- or ReceptorDataset. The differences between this importer and the 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: https://docs.airr-community.org/en/stable/datarep/rearrangements.html

When importing a ReceptorDataset, the AIRR field cell_id is used to determine the chain pairs.

Parameters:

• 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 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 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.

  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:

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/'

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → Dataset

static import_repertoire_dataset(params: dict, dataset_name: str) → RepertoireDataset

static import_sequence_dataset(params: dict, dataset_name: str) → RepertoireDataset

immuneML.IO.dataset_import.ImmuneMLImport module

class immuneML.IO.dataset_import.ImmuneMLImport.ImmuneMLImport

Bases: DataImport

Imports the dataset from the files previously exported by immuneML. It closely resembles AIRR format but relies on binary representations and is optimized for faster read-in at runtime.

ImmuneMLImport can import any kind of dataset (RepertoireDataset, SequenceDataset, ReceptorDataset).

This format includes:

1. a dataset file in yaml format with iml_dataset extension with parameters:

   • name,

   • identifier,

   • metadata_file (for repertoire datasets),

   • metadata_fields (for repertoire datasets),

   • repertoire_ids (for repertoire datasets)

   • element_ids (for receptor and sequence datasets),

   • labels,

2. a csv metadata file (only for repertoire datasets, should be in the same folder as the iml_dataset file),

3. data files for different types of data. For repertoire datasets, data files include one binary numpy file per repertoire with sequences and associated information and one metadata yaml file per repertoire with details such as repertoire identifier, disease status, subject id and other similar available information. For sequence and receptor datasets, sequences or receptors respectively, are stored in batches in binary numpy files.

Parameters:

• path (str) – The path to the previously created dataset file. This file should have an ‘.iml_dataset’ extension. If the path has not been specified, immuneML attempts to load the dataset from a specified metadata file (only for RepertoireDatasets).

• metadata_file (str) – An optional metadata file for a RepertoireDataset. If specified, the RepertoireDataset metadata will be updated to the newly specified metadata without otherwise changing the Repertoire objects

YAML specification:

my_dataset:
    format: ImmuneML
    params:
        path: path/to/dataset.iml_dataset
        metadata_file: path/to/metadata.csv

static import_dataset(params: dict, dataset_name: str) → Dataset

immuneML.IO.dataset_import.ImmunoSEQRearrangementImport module

class immuneML.IO.dataset_import.ImmunoSEQRearrangementImport.ImmunoSEQRearrangementImport

Bases: DataImport

Imports data from Adaptive Biotechnologies immunoSEQ Analyzer rearrangement-level .tsv files into a Repertoire-, or SequenceDataset. RepertoireDatasets should be used when making predictions per repertoire, such as predicting a disease state. SequenceDatasets should be used when predicting values for unpaired (single-chain) immune receptors, like antigen specificity.

The format of the files imported by this importer is described here: https://www.adaptivebiotech.com/wp-content/uploads/2019/07/MRK-00342_immunoSEQ_TechNote_DataExport_WEB_REV.pdf Alternatively, to import sample-level .tsv files, see ImmunoSEQSample import

The only difference between these two importers is which columns they load from the .tsv files.

Parameters:

• 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. 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. Only the files included under the column ‘filename’ are imported into the RepertoireDataset. For setting SequenceDataset metadata, metadata_file is ignored, see metadata_column_mapping instead.

• import_productive (bool) – Whether productive sequences (with value ‘In’ in column frame_type) 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 ‘Stop’ in column frame_type) should be included in the imported sequences. By default, import_with_stop_codon is False.

• import_out_of_frame (bool) – Whether out of frame sequences (with value ‘Out’ in column frame_type) should be included in the imported sequences. 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 immunoSEQ files use 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 RegionType enum.

• column_mapping (dict) –

  A mapping from immunoSEQ column names to immuneML’s internal data representation. For immunoSEQ rearrangement-level files, this is by default set the values shown below in YAML format. A custom column mapping can be specified here if necessary (for example: adding additional data fields if they are present in the file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS.

  rearrangement: sequences
  amino_acid: sequence_aas
  v_gene: v_genes
  j_gene: j_genes
  frame_type: frame_types
  v_family: v_subgroups
  j_family: j_subgroups
  v_allele: v_alleles
  j_allele: j_alleles
  templates: counts
  locus: chains
  

• 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 immunoSEQ rearrangement-level files, this is by default set to:

  v_resolved: v_alleles
  j_resolved: j_alleles
  

• columns_to_load (list) – Specifies which subset of columns must be loaded from the file. By default, this is: [rearrangement, v_family, v_gene, v_allele, j_family, j_gene, j_allele, amino_acid, templates, frame_type, locus]

• metadata_column_mapping (dict) – Specifies metadata for Sequence- and ReceptorDatasets. This should specify a mapping similar to column_mapping where keys are immunoSEQ 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For immunoSEQ rearrangement .tsv files, there is no default metadata_column_mapping.

• separator (str) – Column separator, for ImmunoSEQ files this is by default “t”.

• import_empty_nt_sequences – imports sequences which have an empty nucleotide sequence field; can be True or False

• import_empty_aa_sequences – imports sequences which have an empty amino acid sequence field; can be True or False; for analysis on amino acid sequences, this parameter will typically be False (import only non-empty amino acid sequences)

YAML specification:

my_immunoseq_dataset:
    format: ImmunoSEQRearrangement
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset (True) or a SequenceDataset (False)
        metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset
        metadata_column_mapping: # metadata column mapping ImmunoSEQ: immuneML for SequenceDataset
            immunoseq_column_name1: metadata_label1
            immunoseq_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 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 ImmunoSEQ rearrangement-specific defaults, only change when different behavior is required:
        separator: "\t" # column separator
        columns_to_load: # subset of columns to load
        - rearrangement
        - v_family
        - v_gene
        - v_allele
        - j_family
        - j_gene
        - j_allele
        - amino_acid
        - templates
        - frame_type
        - locus
        region_type: IMGT_CDR3 # what part of the sequence to import
        column_mapping: # column mapping immunoSEQ: immuneML
            rearrangement: sequences
            amino_acid: sequence_aas
            v_gene: v_genes
            j_gene: j_genes
            frame_type: frame_types
            v_family: v_subgroups
            j_family: j_subgroups
            v_allele: v_alleles
            j_allele: j_alleles
            templates: counts
            locus: chains

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → <module 'immuneML.data_model.dataset.Dataset' from '/Users/milenpa/PycharmProjects/BMIimmuneML/immuneML/data_model/dataset/Dataset.py'>

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

immuneML.IO.dataset_import.ImmunoSEQSampleImport module

class immuneML.IO.dataset_import.ImmunoSEQSampleImport.ImmunoSEQSampleImport

Bases: DataImport

Imports data from Adaptive Biotechnologies immunoSEQ Analyzer sample-level .tsv files into a Repertoire-, or SequenceDataset. RepertoireDatasets should be used when making predictions per repertoire, such as predicting a disease state. SequenceDatasets should be used when predicting values for unpaired (single-chain) immune receptors, like antigen specificity.

The format of the files imported by this importer is described here in section 3.4.13 https://clients.adaptivebiotech.com/assets/downloads/immunoSEQ_AnalyzerManual.pdf Alternatively, to import rearrangement-level .tsv files, see ImmunoSEQRearrangement import. The only difference between these two importers is which columns they load from the .tsv files.

Parameters:

• 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. 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. Only the files included under the column ‘filename’ are imported into the RepertoireDataset. For setting SequenceDataset metadata, metadata_file is ignored, see metadata_column_mapping instead.

• import_productive (bool) – Whether productive sequences (with value ‘In’ in column frame_type) 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 ‘Stop’ in column frame_type) should be included in the imported sequences. By default, import_with_stop_codon is False.

• import_out_of_frame (bool) – Whether out of frame sequences (with value ‘Out’ in column frame_type) should be included in the imported sequences. 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 immunoSEQ files use 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 RegionType enum.

• column_mapping (dict) –

  A mapping from immunoSEQ column names to immuneML’s internal data representation. For immunoSEQ sample-level files, this is by default set to the values shown bellow in YAML format. A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS.

  nucleotide: sequences
  aminoAcid: sequence_aas
  vGeneName: v_genes
  jGeneName: j_genes
  sequenceStatus: frame_types
  vFamilyName: v_subgroups
  jFamilyName: j_subgroups
  vGeneAllele: v_alleles
  jGeneAllele: j_alleles
  count (templates/reads): counts
  

• 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 immunoSEQ sample .tsv files, there is no default column_mapping_synonyms.

• columns_to_load (list) – Specifies which subset of columns must be loaded from the file. By default, this is: [nucleotide, aminoAcid, count (templates/reads), vFamilyName, vGeneName, vGeneAllele, jFamilyName, jGeneName, jGeneAllele, sequenceStatus]; these are the columns from the original file that will be imported

• metadata_column_mapping (dict) – Specifies metadata for Sequence- and ReceptorDatasets. This should specify a mapping similar to column_mapping where keys are immunoSEQ 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For immunoSEQ sample .tsv files, there is no default metadata_column_mapping.

• separator (str) – Column separator, for ImmunoSEQ files this is by default “t”.

YAML specification:

my_immunoseq_dataset:
    format: ImmunoSEQSample
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset (True) or a SequenceDataset (False)
        metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset
        metadata_column_mapping: # metadata column mapping ImmunoSEQ: immuneML for SequenceDataset
            immunoseq_column_name1: metadata_label1
            immunoseq_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 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 ImmunoSEQ sample-specific defaults, only change when different behavior is required:
        separator: "\t" # column separator
        columns_to_load: # subset of columns to load
        - nucleotide
        - aminoAcid
        - count (templates/reads)
        - vFamilyName
        - vGeneName
        - vGeneAllele
        - jFamilyName
        - jGeneName
        - jGeneAllele
        - sequenceStatus
        region_type: IMGT_CDR3 # what part of the sequence to import
        column_mapping: # column mapping immunoSEQ: immuneML
            nucleotide: sequences
            aminoAcid: sequence_aas
            vGeneName: v_genes
            jGeneName: j_genes
            sequenceStatus: frame_types
            vFamilyName: v_subgroups
            jFamilyName: j_subgroups
            vGeneAllele: v_alleles
            jGeneAllele: j_alleles
            count (templates/reads): counts

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → Dataset

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

immuneML.IO.dataset_import.MiXCRImport module

class immuneML.IO.dataset_import.MiXCRImport.MiXCRImport

Bases: DataImport

Imports data in MiXCR format into a Repertoire-, or SequenceDataset. RepertoireDatasets should be used when making predictions per repertoire, such as predicting a disease state. SequenceDatasets should be used when predicting values for unpaired (single-chain) immune receptors, like antigen specificity.

Parameters:

• path (str) – For RepertoireDatasets, this is the path to a directory with MiXCR 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. 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. Only the MiXCR files included under the column ‘filename’ are imported into the RepertoireDataset. For setting SequenceDataset metadata, metadata_file is ignored, see metadata_column_mapping instead.

• 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, such as ‘_’, 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 MiXCR uses IMGT junction as CDR3. Alternatively to importing the CDR3 sequence, other region types can be specified here as well. Valid values for region_type are defined in MiXCRImport.SEQUENCE_NAME_MAP.

• column_mapping (dict) –

  A mapping from MiXCR column names to immuneML’s internal data representation. The columns that specify the sequences to import are handled by the region_type parameter. A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the MiXCR file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS. For MiXCR, this is by default set to:

  cloneCount: counts
  allVHitsWithScore: v_alleles
  allJHitsWithScore: j_alleles
  

• 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 MiXCR format, there is no default column_mapping_synonyms.

• columns_to_load (list) – Specifies which subset of columns must be loaded from the MiXCR file. By default, this is: [cloneCount, allVHitsWithScore, allJHitsWithScore, aaSeqCDR3, nSeqCDR3]

• metadata_column_mapping (dict) – Specifies metadata for Sequence- and ReceptorDatasets. This should specify a mapping similar to column_mapping where keys are MiXCR 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For MiXCR format, there is no default metadata_column_mapping.

• separator (str) – Column separator, for MiXCR this is by default “t”.

YAML specification:

my_mixcr_dataset:
    format: MiXCR
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset (True) or a SequenceDataset (False)
        metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset
        metadata_column_mapping: # metadata column mapping MiXCR: immuneML for SequenceDataset
            mixcrColumnName1: metadata_label1
            mixcrColumnName2: metadata_label2
        region_type: IMGT_CDR3 # what part of the sequence to import
        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 MiXCR-specific defaults, only change when different behavior is required:
        separator: "\t" # column separator
        columns_to_load: # subset of columns to load, sequence columns are handled by region_type parameter
        - cloneCount
        - allVHitsWithScore
        - allJHitsWithScore
        - aaSeqCDR3
        - nSeqCDR3
        column_mapping: # column mapping MiXCR: immuneML
            cloneCount: counts
            allVHitsWithScore: v_genes
            allJHitsWithScore: j_genes

SEQUENCE_NAME_MAP = {RegionType.IMGT_CDR1: {'AA': 'aaSeqCDR1', 'NT': 'nSeqCDR1'}, RegionType.IMGT_CDR2: {'AA': 'aaSeqCDR2', 'NT': 'nSeqCDR2'}, RegionType.IMGT_CDR3: {'AA': 'aaSeqCDR3', 'NT': 'nSeqCDR3'}, RegionType.IMGT_FR1: {'AA': 'aaSeqFR1', 'NT': 'nSeqFR1'}, RegionType.IMGT_FR2: {'AA': 'aaSeqFR2', 'NT': 'nSeqFR2'}, RegionType.IMGT_FR3: {'AA': 'aaSeqFR3', 'NT': 'nSeqFR3'}, RegionType.IMGT_FR4: {'AA': 'aaSeqFR4', 'NT': 'nSeqFR4'}}

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → <module 'immuneML.data_model.dataset.Dataset' from '/Users/milenpa/PycharmProjects/BMIimmuneML/immuneML/data_model/dataset/Dataset.py'>

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

Function for loading the data from one MiXCR file, such that:

• for the given region (CDR3/full sequence), both nucleotide and amino acid sequence are loaded

• if the region is CDR3, it adapts the sequence to the definition of the CDR3 (IMGT junction vs IMGT CDR3)

• the chain for each sequence is extracted from the v gene name

• the genes are loaded from the top score for gene without allele info

Parameters:

• df – the dataframe as imported from the csv file

• params – DatasetImportParams object defining what to import and how to do it

Returns:

dataframe corresponding to Repertoire.FIELDS and custom lists which can be used to create a Repertoire object

immuneML.IO.dataset_import.OLGAImport module

class immuneML.IO.dataset_import.OLGAImport.OLGAImport

Bases: DataImport

Imports data generated by OLGA simulations into a Repertoire-, or SequenceDataset. Assumes that the columns in each file correspond to: nucleotide sequences, amino acid sequences, v genes, j genes

Reference: Sethna, Zachary et al. ‘High-throughput immune repertoire analysis with IGoR’. Bioinformatics, (2019) doi.org/10.1093/bioinformatics/btz035.

Parameters:

• path (str) – For RepertoireDatasets, this is the path to a directory with OLGA 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. 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. Only the OLGA files included under the column ‘filename’ are imported into the RepertoireDataset. SequenceDataset metadata is currently not supported.

• 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 OLGA 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 RegionType enum.

• separator (str) – Column separator, for OLGA this is by default “t”.

• column_mapping (dict) – defines which columns to import from olga format: keys are the number of the columns and values are the names of the columns to be mapped to

YAML specification:

my_olga_dataset:
    format: OLGA
    params:
        path: path/to/files/
        is_repertoire: True # whether to import a RepertoireDataset (True) or a SequenceDataset (False)
        metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset
        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 OLGA-specific defaults, only change when different behavior is required:
        separator: "\t" # column separator
        region_type: IMGT_CDR3 # what part of the sequence to import
        columns_to_load: [0, 1, 2, 3]
        column_mapping:
            0: sequences
            1: sequence_aas
            2: v_genes
            3: j_genes

static alternative_load_func(filepath, params)

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → <module 'immuneML.data_model.dataset.Dataset' from '/Users/milenpa/PycharmProjects/BMIimmuneML/immuneML/data_model/dataset/Dataset.py'>

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

immuneML.IO.dataset_import.RandomReceptorDatasetImport module

class immuneML.IO.dataset_import.RandomReceptorDatasetImport.RandomReceptorDatasetImport

Bases: DataImport

Returns a ReceptorDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.

Parameters:

• receptor_count (int) – The number of receptors the ReceptorDataset should contain.

• chain_1_length_probabilities (dict) –

  A mapping where the keys correspond to different sequence lengths for chain 1, and the values are the probabilities for choosing each sequence length. For example, to create a random ReceptorDataset where 40% of the sequences for chain 1 would be of length 10, and 60% of the sequences would have length 12, this mapping would need to be specified:

  10: 0.4
  12: 0.6
  

• chain_2_length_probabilities (dict) – Same as chain_1_length_probabilities, but for chain 2.

• labels (dict) –

  A mapping that specifies randomly chosen labels to be assigned to the receptors. One or multiple labels can be specified here. The keys of this mapping are the labels, and the values consist of another mapping between label classes and their probabilities. For example, to create a random ReceptorDataset with the label cmv_epitope where 70% of the receptors has class binding and the remaining 30% has class not_binding, the following mapping should be specified:

  cmv_epitope:
      binding: 0.7
      not_binding: 0.3
  

YAML specification:

my_random_dataset:
    format: RandomReceptorDataset
    params:
        receptor_count: 100 # number of random receptors to generate
        chain_1_length_probabilities:
            14: 0.8 # 80% of all generated sequences for all receptors (for chain 1) will have length 14
            15: 0.2 # 20% of all generated sequences across all receptors (for chain 1) will have length 15
        chain_2_length_probabilities:
            14: 0.8 # 80% of all generated sequences for all receptors (for chain 2) will have length 14
            15: 0.2 # 20% of all generated sequences across all receptors (for chain 2) will have length 15
        labels:
            epitope1: # label name
                True: 0.5 # 50% of the receptors will have class True
                False: 0.5 # 50% of the receptors will have class False
            epitope2: # next label with classes that will be assigned to receptors independently of the previous label or other parameters
                1: 0.3 # 30% of the generated receptors will have class 1
                0: 0.7 # 70% of the generated receptors will have class 0

static import_dataset(params, name: str) → ReceptorDataset

Returns randomly generated receptor dataset according to the parameters;

YAML specification:

result_path: path/where/to/store/results/ receptor_count: 100 # number of random receptors to generate chain_1_length_probabilities:

14: 0.8 # 80% of all generated sequences for all receptors (for chain 1) will have length 14 15: 0.2 # 20% of all generated sequences across all receptors (for chain 1) will have length 15

chain_2_length_probabilities:

14: 0.8 # 80% of all generated sequences for all receptors (for chain 2) will have length 14 15: 0.2 # 20% of all generated sequences across all receptors (for chain 2) will have length 15

labels:

epitope1: # label name

True: 0.5 # 50% of the receptors will have class True False: 0.5 # 50% of the receptors will have class False

epitope2: # next label with classes that will be assigned to receptors independently of the previous label or other parameters

1: 0.3 # 30% of the generated receptors will have class 1 0: 0.7 # 70% of the generated receptors will have class 0

immuneML.IO.dataset_import.RandomRepertoireDatasetImport module

class immuneML.IO.dataset_import.RandomRepertoireDatasetImport.RandomRepertoireDatasetImport

Bases: DataImport

Returns a RepertoireDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.

Parameters:

• repertoire_count (int) – The number of repertoires the RepertoireDataset should contain.

• sequence_count_probabilities (dict) –

  A mapping where the keys are the number of sequences per repertoire, and the values are the probabilities that any of the repertoires would have that number of sequences. For example, to create a random RepertoireDataset where 40% of the repertoires would have 1000 sequences, and the other 60% would have 1100 sequences, this mapping would need to be specified:

  1000: 0.4
  1100: 0.6
  

• sequence_length_probabilities (dict) –

  A mapping where the keys correspond to different sequence lengths, and the values are the probabilities for choosing each sequence length. For example, to create a random RepertoireDataset where 40% of the sequences would be of length 10, and 60% of the sequences would have length 12, this mapping would need to be specified:

  10: 0.4
  12: 0.6
  

• labels (dict) –

  A mapping that specifies randomly chosen labels to be assigned to the Repertoires. One or multiple labels can be specified here. The keys of this mapping are the labels, and the values consist of another mapping between label classes and their probabilities. For example, to create a random RepertoireDataset with the label CMV where 70% of the Repertoires has class cmv_positive and the remaining 30% has class cmv_negative, the following mapping should be specified:

  CMV:
      cmv_positive: 0.7
      cmv_negative: 0.3
  

YAML specification:

my_random_dataset:
    format: RandomRepertoireDataset
    params:
        repertoire_count: 100 # number of random repertoires to generate
        sequence_count_probabilities:
            10: 0.5 # probability that any of the repertoires would have 10 receptor sequences
            20: 0.5
        sequence_length_probabilities:
            10: 0.5 # probability that any of the receptor sequences would be 10 amino acids in length
            12: 0.5
        labels: # randomly assigned labels (only useful for simple benchmarking)
            cmv:
                True: 0.5 # probability of value True for label cmv to be assigned to any repertoire
                False: 0.5

static import_dataset(params: dict, dataset_name: str) → RepertoireDataset

immuneML.IO.dataset_import.RandomSequenceDatasetImport module

class immuneML.IO.dataset_import.RandomSequenceDatasetImport.RandomSequenceDatasetImport

Bases: DataImport

Returns a SequenceDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.

Parameters:

• sequence_count (int) – The number of sequences the SequenceDataset should contain.

• length_probabilities (dict) –

  A mapping where the keys correspond to different sequence lengths and the values are the probabilities for choosing each sequence length. For example, to create a random SequenceDataset where 40% of the sequences would be of length 10, and 60% of the sequences would have length 12, this mapping would need to be specified:

  10: 0.4
  12: 0.6
  

• labels (dict) –

  A mapping that specifies randomly chosen labels to be assigned to the sequences. One or multiple labels can be specified here. The keys of this mapping are the labels, and the values consist of another mapping between label classes and their probabilities. For example, to create a random SequenceDataset with the label cmv_epitope where 70% of the sequences has class binding and the remaining 30% has class not_binding, the following mapping should be specified:

  cmv_epitope:
      binding: 0.7
      not_binding: 0.3
  

YAML specification:

my_random_dataset:
    format: RandomSequenceDataset
    params:
        sequence_count: 100 # number of random sequences to generate
        length_probabilities:
            14: 0.8 # 80% of all generated sequences for all sequences will have length 14
            15: 0.2 # 20% of all generated sequences across all sequences will have length 15
        labels:
            epitope1: # label name
                True: 0.5 # 50% of the sequences will have class True
                False: 0.5 # 50% of the sequences will have class False
            epitope2: # next label with classes that will be assigned to sequences independently of the previous label or other parameters
                1: 0.3 # 30% of the generated sequences will have class 1
                0: 0.7 # 70% of the generated sequences will have class 0

static import_dataset(params, name: str) → SequenceDataset

Returns randomly generated receptor dataset according to the parameters;

YAML specification:

result_path: path/where/to/store/results/ sequence_count: 100 # number of random sequences to generate chain_1_length_probabilities:

14: 0.8 # 80% of all generated sequences for all sequences will have length 14 15: 0.2 # 20% of all generated sequences across all sequences will have length 15

labels:

epitope1: # label name

True: 0.5 # 50% of the sequences will have class True False: 0.5 # 50% of the sequences will have class False

epitope2: # next label with classes that will be assigned to sequences independently of the previous label or other parameters

1: 0.3 # 30% of the generated sequences will have class 1 0: 0.7 # 70% of the generated sequences will have class 0

immuneML.IO.dataset_import.SingleLineReceptorImport module

class immuneML.IO.dataset_import.SingleLineReceptorImport.SingleLineReceptorImport

Bases: DataImport

Imports data from a tabular file (where each line contains a pair of immune receptor sequences) into a ReceptorDataset. If you instead want to import a ReceptorDataset from a tabular file that contains one receptor sequence per line, see Generic import.

Parameters:

• path (str) – Required parameter. This is the path to a directory with files to import.

• receptor_chains (str) – Required parameter. Determines which pair of chains to import for each Receptor. Valid values for receptor_chains are the names of the ChainPair enum.

• 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. When IMGT_CDR3 is specified, 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 RegionType enum.

• column_mapping (dict) –

  A mapping where the keys are the column names in the input file, and the values must be mapped to the following fields: <chain>_amino_acid_sequence, <chain>_nucleotide_sequence, <chain>_v_gene, <chain>_j_gene, identifier, epitope. The possible names that can be filled in for <chain> are given in Chain Any column namme other than the sequence, v/j genes and identifier will be set as metadata fields to the Receptors, and can subsequently be used as labels in immuneML instructions. For TCR alpha-beta receptor import, a column mapping could for example look like this:

  cdr3_a_aa: alpha_amino_acid_sequence
  cdr3_b_aa: beta_amino_acid_sequence
  cdr3_a_nucseq: alpha_nucleotide_sequence
  cdr3_b_nucseq: beta_nucleotide_sequence
  v_a_gene: alpha_v_gene
  v_b_gene: beta_v_gene
  j_a_gene: alpha_j_gene
  j_b_gene: beta_j_gene
  clone_id: identifier
  epitope: epitope # metadata field
  

• 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.

• 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 “,”.

• organism (str) – The organism that the receptors came from. This will be set as a parameter in the ReceptorDataset object.

YAML specification:

my_receptor_dataset:
    format: SingleLineReceptor
    params:
        path: path/to/files/
        receptor_chains: TRA_TRB # what chain pair to import
        separator: "\t" # column separator
        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
        region_type: IMGT_CDR3 # what part of the sequence to import
        columns_to_load: # which subset of columns to load from the file
        - subject
        - epitope
        - count
        - v_a_gene
        - j_a_gene
        - cdr3_a_aa
        - v_b_gene
        - j_b_gene
        - cdr3_b_aa
        - clone_id
        column_mapping: # column mapping file: immuneML
            cdr3_a_aa: alpha_amino_acid_sequence
            cdr3_b_aa: beta_amino_acid_sequence
            cdr3_a_nucseq: alpha_nucleotide_sequence
            cdr3_b_nucseq: beta_nucleotide_sequence
            v_a_gene: alpha_v_gene
            v_b_gene: beta_v_gene
            j_a_gene: alpha_j_gene
            j_b_gene: beta_j_gene
            clone_id: identifier
            epitope: epitope
            organism: mouse

static get_documentation()

static import_dataset(params, dataset_name: str) → ReceptorDataset

static make_gene_columns(df: pandas.DataFrame, genes: list, chain_name=None)

immuneML.IO.dataset_import.TenxGenomicsImport module

class immuneML.IO.dataset_import.TenxGenomicsImport.TenxGenomicsImport

Bases: 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: https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/output/annotation#consensus

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.

Parameters:

• path (str) – For RepertoireDatasets, this is the path to a directory with 10xGenomics 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 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 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 RegionType enum.

• column_mapping (dict) –

  A mapping from 10xGenomics column names to immuneML’s internal data representation. 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. For 10xGenomics, this is by default set to:

  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
  

• 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. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For 10xGenomics format, there is no default metadata_column_mapping.

• separator (str) – Column separator, for 10xGenomics this is by default “,”.

YAML specification:

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

static get_documentation()

static import_dataset(params: dict, dataset_name: str) → Dataset

static import_receptors(df, params)

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

immuneML.IO.dataset_import.VDJdbImport module

class immuneML.IO.dataset_import.VDJdbImport.VDJdbImport

Bases: DataImport

Imports data in VDJdb format 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.

Arguments:

path (str): For RepertoireDatasets, this is the path to a directory with VDJdb 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 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 VDJdb column named ‘complex.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 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 VDJdb 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 RegionType enum.

column_mapping (dict): A mapping from VDJdb column names to immuneML’s internal data representation. A custom column mapping can be specified here if necessary (for example; adding additional data fields if they are present in the VDJdb file, or using alternative column names). Valid immuneML fields that can be specified here are defined by Repertoire.FIELDS. For VDJdb, this is by default set to:

V: v_alleles
J: j_alleles
CDR3: sequence_aas
complex.id: sequence_identifiers
Gene: chains

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 VDJdb 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 VDJdb column names and values are the names that are internally used in immuneML as metadata fields. This means that epitope, epitope_gene and epitope_species can be used as prediction labels for Sequence- and ReceptorDatasets. This parameter can also be used to specify sequence-level metadata columns for RepertoireDatasets, which can be used by reports. To set prediction label metadata for RepertoireDatasets, see metadata_file instead. For VDJdb format, this parameter is by default set to:

Epitope: epitope
Epitope gene: epitope_gene
Epitope species: epitope_species

separator (str): Column separator, for VDJdb this is by default “t”.

YAML specification:

my_vdjdb_dataset:
    format: VDJdb
    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
        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 VDJdb-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 VDJdb: immuneML
            V: v_genes
            J: j_genes
            CDR3: sequence_aas
            complex.id: sequence_identifiers
            Gene: chains
        metadata_column_mapping: # metadata column mapping VDJdb: immuneML
            Epitope: epitope
            Epitope gene: epitope_gene
            Epitope species: epitope_species

KEY_MAPPING = {'subject.id': 'subject_id'}

static extract_meta_columns(df: pandas.DataFrame, params) → pandas.DataFrame

extracts values from meta columns in VDJdb format to separate columns in the data frame, using VDJdbImport.KEY_MAPPING

Note: the KEY_MAPPING values cannot be manually set, but correspond to the standard names as defined by the AIRR Community

Parameters:

• df – data frame of from file[s] in VDJdb which have already been preprocessed

• params – parameters from importing dataset which includes the information on metadata column mapping

Returns:

the data frame with additional columns where the metadata (if present) were extracted

static get_documentation()

static get_sequence_identifiers(receptor_identifiers, chains)

static import_dataset(params: dict, dataset_name: str) → Dataset

static import_receptors(df, params)

static preprocess_dataframe(df: pandas.DataFrame, params: DatasetImportParams)

Module contents