immuneML.IO.dataset_import package¶
Submodules¶
immuneML.IO.dataset_import.AIRRImport module¶
- class immuneML.IO.dataset_import.AIRRImport.AIRRImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
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 Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
paired (str): Required for Sequence- or ReceptorDatasets. This parameter determines whether to import a SequenceDataset (paired = False) or a ReceptorDataset (paired = True). In a ReceptorDataset, two sequences with chain types specified by receptor_chains are paired together based on 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
ChainPairenum. 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_unknown_productivity (bool): Whether sequences with unknown productivity (missing value in column productive) should be included in the imported sequences. By default, import_unknown_productivity 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.
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).
additional_column_in_the_file: column_name_to_be_used_in_analysis
separator (str): Column separator, for AIRR this is by default “t”.
YAML specification:
definitions: datasets: 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 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 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 check for import
immuneML.IO.dataset_import.DataImport module¶
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.SequenceParams.ChainPair = None, result_path: pathlib.Path = None, columns_to_load: list = None, label_columns: list = None, separator: str = None, column_mapping: dict = None, column_mapping_synonyms: dict = None, region_type: immuneML.data_model.SequenceParams.RegionType = None, import_productive: bool = None, import_unknown_productivity: bool = None, import_unproductive: bool = None, import_with_stop_codon: bool = None, import_out_of_frame: bool = None, import_illegal_characters: bool = 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, dataset_file: pathlib.Path = None)[source]¶
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)[source]¶
- column_mapping: dict = None¶
- column_mapping_synonyms: dict = None¶
- columns_to_load: list = None¶
- dataset_file: Path = 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_unknown_productivity: bool = None¶
- import_unproductive: bool = None¶
- import_with_stop_codon: bool = None¶
- is_repertoire: bool = None¶
- label_columns: list = 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(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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
Specification arguments:
path (str): For RepertoireDatasets, this is the path to a directory with files to import. For Sequence- or ReceptorDatasets this path may either be the path to the file to import, or the path to the folder locating one or multiple files with .tsv, .csv or .txt extensions. By default path is set to the current working directory.
is_repertoire (bool): If True, this imports a RepertoireDataset. If False, it imports a SequenceDataset or ReceptorDataset. By default, is_repertoire is set to True.
metadata_file (str): Required for RepertoireDatasets. This parameter specifies the path to the metadata file. This is a csv file with columns filename, subject_id and arbitrary other columns which can be used as labels in instructions. For setting Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
paired (str): Required for Sequence- or ReceptorDatasets. This parameter determines whether to import a SequenceDataset (paired = False) or a ReceptorDataset (paired = True). In a ReceptorDataset, two sequences with chain types specified by receptor_chains are paired together based on a common identifier. This identifier should be mapped to the immuneML field ‘sequence_identifiers’ using the column_mapping.
receptor_chains (str): Required for ReceptorDatasets. Determines which pair of chains to import for each Receptor. Valid values for receptor_chains are the names of the ChainPair enum.
import_illegal_characters (bool): Whether to import sequences that contain illegal characters, i.e., characters that do not appear in the sequence alphabet (amino acids including stop codon ‘*’, or nucleotides). When set to false, filtering is only applied to the sequence type of interest (when running immuneML in amino acid mode, only entries with illegal characters in the amino acid sequence are removed). By default import_illegal_characters is False.
import_empty_nt_sequences (bool): imports sequences which have an empty nucleotide sequence field; can be True or False. By default, import_empty_nt_sequences is set to True.
import_empty_aa_sequences (bool): imports sequences which have an empty amino acid sequence field; can be True or False; for analysis on amino acid sequences, this parameter should be False (import only non-empty amino acid sequences). By default, import_empty_aa_sequences is set to False.
region_type (str): Which part of the sequence to import. By default, this value is set to IMGT_CDR3. This means immuneML assumes the IMGT junction (including leading C and trailing Y/F amino acids) is used in the input file, and the first and last amino acids will be removed from the sequences to retrieve the IMGT CDR3 sequence. Specifying any other value will result in importing the sequences as they are. Valid values for region_type are the names of the
RegionTypeenum.column_mapping (dict): Required for all datasets. A mapping where the keys are the column names in the input file, and the values correspond to the names in the AIRR format. A column mapping can look for example like this:
file_column_amino_acids: cdr3_aa file_column_v_genes: v_call file_column_j_genes: j_call file_column_frequencies: duplicate_count
column_mapping_synonyms (dict): This is a column mapping that can be used if a column could have alternative names. The formatting is the same as column_mapping. If some columns specified in column_mapping are not found in the file, the columns specified in column_mapping_synonyms are instead attempted to be loaded. For Generic import, there is no default column_mapping_synonyms.
columns_to_load (list): Optional; specifies which columns to load from the input file. This may be useful if the input files contain many unused columns. If no value is specified, all columns are loaded.
separator (str): Required parameter. Column separator, for example “t” or “,”. The default value is “t”
YAML specification:
definitions: datasets: my_generic_dataset: format: Generic params: path: path/to/files/ is_repertoire: True # whether to import a RepertoireDataset metadata_file: path/to/metadata.csv # metadata file for RepertoireDataset paired: False # whether to import SequenceDataset (False) or ReceptorDataset (True) when is_repertoire = False receptor_chains: TRA_TRB # what chain pair to import for a ReceptorDataset separator: "\t" # column separator import_illegal_characters: False # remove sequences with illegal characters for the sequence_type being used import_empty_nt_sequences: True # keep sequences even though the nucleotide sequence might be empty import_empty_aa_sequences: False # filter out sequences if they don't have amino acid sequence set region_type: IMGT_CDR3 # which column to check for illegal characters/empty strings etc column_mapping: # column mapping file: immuneML/AIRR column names file_column_amino_acids: junction_aa file_column_v_genes: v_call file_column_j_genes: j_call file_column_frequencies: duplicate_count file_column_antigen_specificity: antigen_specificity columns_to_load: # which subset of columns to load from the file - file_column_amino_acids - file_column_v_genes - file_column_j_genes - file_column_frequencies - file_column_antigen_specificity
immuneML.IO.dataset_import.IGoRImport module¶
- class immuneML.IO.dataset_import.IGoRImport.IGoRImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
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 Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
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 check when importing. 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
RegionTypeenum.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: cdr3 seq_index: sequence_id
separator (str): Column separator, for IGoR this is by default “,”.
YAML specification:
definitions: datasets: 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 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: cdr3 seq_index: sequence_id igor_column_name1: metadata_label1 igor_column_name2: metadata_label2
- 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 translate_sequence(nt_seq)[source]¶
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(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
path (str): This is the path to a directory with .zip files retrieved from the iReceptor Gateway. These .zip files should include AIRR files (with .tsv extension) and corresponding metadata.json files with matching names (e.g., for my_dataset.tsv the corresponding metadata file is called my_dataset-metadata.json). The zip files must use the .zip extension.
is_repertoire (bool): If True, this imports a RepertoireDataset. If False, it imports a SequenceDataset or ReceptorDataset. By default, is_repertoire is set to True.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For RepertoireDataset labels, label_columns is ignored, metadata is discovered automatically from the metadata json.
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
ChainPairenum. 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
RegionTypeenum.separator (str): Column separator, for AIRR this is by default “t”.
YAML specification:
definitions: datasets: 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
- REPERTOIRES_FOLDER = 'repertoires/'¶
immuneML.IO.dataset_import.ImmuneMLImport module¶
immuneML.IO.dataset_import.ImmunoSEQRearrangementImport module¶
- class immuneML.IO.dataset_import.ImmunoSEQRearrangementImport.ImmunoSEQRearrangementImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
path (str): For RepertoireDatasets, this is the path to a directory with files to import. For Sequence- or ReceptorDatasets this path may either be the path to the file to import, or the path to the folder locating one or multiple files with .tsv, .csv or .txt extensions. By default path is set to the current working directory.
is_repertoire (bool): If True, this imports a RepertoireDataset. If False, it imports a SequenceDataset. 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 Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
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 check when importing. 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
RegionTypeenum.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: sequence amino_acid: junction_aa v_resolved: v_call j_resolved: j_call templates: duplicate_count
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]
separator (str): Column separator, for ImmunoSEQ files this is by default “t”.
import_empty_nt_sequences (bool): imports sequences which have an empty nucleotide sequence field; can be True or False
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 will typically be False (import only non-empty amino acid sequences)
YAML specification:
definitions: datasets: 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_resolved - j_family - j_gene - j_resolved - amino_acid - templates - frame_type - locus region_type: IMGT_CDR3 # what part of the sequence to import column_mapping: # column mapping immunoSEQ: immuneML rearrangement: cdr3 amino_acid: cdr3_aa v_resolved: v_call j_resolved: j_call templates: duplicate_count
immuneML.IO.dataset_import.ImmunoSEQSampleImport module¶
- class immuneML.IO.dataset_import.ImmunoSEQSampleImport.ImmunoSEQSampleImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
path (str): For RepertoireDatasets, this is the path to a directory with files to import. For Sequence- or ReceptorDatasets this path may either be the path to the file to import, or the path to the folder locating one or multiple files with .tsv, .csv or .txt extensions. By default path is set to the current working directory.
is_repertoire (bool): If True, this imports a RepertoireDataset. If False, it imports a SequenceDataset. 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 Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
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
RegionTypeenum.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: cdr3 aminoAcid: cdr3_aa count (templates/reads): duplicate_count
column_mapping_synonyms (dict): This is a column mapping that can be used if a column could have alternative names. The formatting is the same as column_mapping. If some columns specified in column_mapping are not found in the file, the columns specified in column_mapping_synonyms are instead attempted to be loaded. For 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:
definitions: datasets: 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: sequence aminoAcid: junction_aa vGeneName: v_call jGeneName: j_call sequenceStatus: frame_type vFamilyName: v_family jFamilyName: j_family vGeneAllele: v_allele jGeneAllele: j_allele count (templates/reads): duplicate_count
immuneML.IO.dataset_import.MiXCRImport module¶
- class immuneML.IO.dataset_import.MiXCRImport.MiXCRImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
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 Sequence- or ReceptorDataset labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
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 IMGT_CDR3, IMGT_JUNCTION, IMGT_CDR1, IMGT_CDR2, IMGT_FR1, IMGT_FR2, IMGT_FR3, IMGT_FR4.
column_mapping (dict): A mapping from MiXCR column names to immuneML’s 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). immuneML uses fields as defined in the AIRR schema. For MiXCR, this is by default set to:
cloneCount: duplicate_count allVHitsWithScore: v_call allJHitsWithScore: j_call
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]
separator (str): Column separator, for MiXCR this is by default “t”.
YAML specification:
definitions: datasets: 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 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: duplicate_count allVHitsWithScore: v_call allJHitsWithScore: j_call mixcrColumnName1: metadata_label1 mixcrColumnName2: metadata_label2
immuneML.IO.dataset_import.OLGAImport module¶
- class immuneML.IO.dataset_import.OLGAImport.OLGAImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
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
RegionTypeenum.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:
definitions: datasets: 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 amino acid sequence set # Optional fields with OLGA-specific defaults, only change when different behavior is required: separator: "\t" # column separator columns_to_load: [0, 1, 2, 3] column_mapping: 0: junction 1: junction_aa 2: v_call 3: j_call
immuneML.IO.dataset_import.RandomReceptorDatasetImport module¶
- class immuneML.IO.dataset_import.RandomReceptorDatasetImport.RandomReceptorDatasetImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportReturns a ReceptorDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.
Specification arguments:
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:
definitions: datasets: 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
- import_dataset() ReceptorDataset[source]¶
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(params: dict, dataset_name: str)[source]¶
Bases:
DataImportReturns a RepertoireDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.
Specification arguments:
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:
definitions: datasets: 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
immuneML.IO.dataset_import.RandomSequenceDatasetImport module¶
- class immuneML.IO.dataset_import.RandomSequenceDatasetImport.RandomSequenceDatasetImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportReturns a SequenceDataset consisting of randomly generated sequences, which can be used for benchmarking purposes. The sequences consist of uniformly chosen amino acids or nucleotides.
Specification arguments:
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
region_type (str): which region_type to assign to all randomly generated sequences
YAML specification:
definitions: datasets: 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
- import_dataset() SequenceDataset[source]¶
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¶
immuneML.IO.dataset_import.TenxGenomicsImport module¶
- class immuneML.IO.dataset_import.TenxGenomicsImport.TenxGenomicsImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification arguments:
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 labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
paired (str): Required for Sequence- or ReceptorDatasets. This parameter determines whether to import a SequenceDataset (paired = False) or a ReceptorDataset (paired = True). In a ReceptorDataset, two sequences with chain types specified by receptor_chains are paired together based on 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
ChainPairenum. 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 ‘True’ in column productive) should be included in the imported sequences. By default, import_productive is True.
import_unproductive (bool): Whether productive sequences (with value ‘Fale’ in column productive) should be included in the imported sequences. By default, import_unproductive is False.
import_unknown_productivity (bool): Whether sequences with unknown productivity (missing or ‘NA’ value in column productive) should be included in the imported sequences. By default, import_unknown_productivity is True.
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
RegionTypeenum.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: junction cdr3_nt: junction_aa v_gene: v_call j_gene: j_call umis: duplicate_count clonotype_id: cell_id consensus_id: sequence_id
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.
separator (str): Column separator, for 10xGenomics this is by default “,”.
YAML specification:
definitions: datasets: 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 import_illegal_characters: False # remove sequences with illegal characters for the sequence_type being used import_empty_nt_sequences: True # keep sequences even though the nucleotide sequence might be empty import_empty_aa_sequences: False # filter out sequences if they don't have amino acid sequence set # 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: junction_aa cdr3_nt: junction v_gene: v_call j_gene: j_call umis: duplicate_count clonotype_id: cell_id consensus_id: sequence_id
immuneML.IO.dataset_import.VDJdbImport module¶
- class immuneML.IO.dataset_import.VDJdbImport.VDJdbImport(params: dict, dataset_name: str)[source]¶
Bases:
DataImportImports 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.
Specification 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 labels, metadata_file is ignored, use label_columns instead.
label_columns (list): For Sequence- or ReceptorDataset, this parameter can be used to explicitly set the column names of labels to import. By default, label_columns for VDJdbImport are [Epitope, Epitope gene, Epitope species]. These labels can be used as prediction target. When label_columns are not set, label names are attempted to be discovered automatically (any column name which is not used in the column_mapping). For setting RepertoireDataset labels, label_columns is ignored, use metadata_file instead.
paired (str): Required for Sequence- or ReceptorDatasets. This parameter determines whether to import a SequenceDataset (paired = False) or a ReceptorDataset (paired = True). In a ReceptorDataset, two sequences with chain types specified by receptor_chains are paired together based on 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
ChainPairenum. 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
RegionTypeenum.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_call J: j_call CDR3: junction_aa complex.id: cell_id Gene: locus
separator (str): Column separator, for VDJdb this is by default “t”.
YAML specification:
definitions: datasets: 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 amino acid sequence 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_call J: j_call CDR3: junction_aa complex.id: sequence_id Gene: chain Epitope: epitope Epitope gene: epitope_gene Epitope species: epitope_species
- KEY_MAPPING = {'subject.id': 'subject_id'}¶
- extract_dict_columns(df: pandas.DataFrame) pandas.DataFrame[source]¶
extracts values from meta columns in VDJdb format to separate columns in the data frame, using VDJdbImport.KEY_MAPPING
- Parameters:
df – data frame of from file[s] in VDJdb which have already been preprocessed
- Returns:
the data frame with additional columns where the metadata (if present) were extracted