mutcleaner.cleaners.trpb_cleaner#

Functions

clean_trpb_dataset(pipeline)

Clean TrpB dataset using configurable pipeline

create_trpb_cleaner(dataset_or_path[, config])

Create TrpB dataset cleaning pipeline

Classes

TrpBCleanerConfig([pipeline_name, ...])

Configuration class for TrpB dataset cleaner.
class mutcleaner.cleaners.trpb_cleaner.TrpBCleanerConfig(pipeline_name='TrpB Cleaning Pipeline', num_workers=16, validate_config=True, column_mapping=<factory>, filters=<factory>, type_conversions=<factory>, wt_sequence='MKGYFGPYGGQYVPEILMGALEELEAAYEGIMKDESFWKEFNDLLRDYAGRPTPLYFARRLSEKYGARVYLKREDLLHTGAHKINNAIGQVLLAKLMGKTRIIAETGAGQHGVATATAAALFGMECVIYMGEEDTIRQKLNVERMKLLGAKVVPVKSGSRTLKDAIDEALRDWITNLQTTYYVFGSVVGPHPYPIIVRNFQKVIGEETKKQIPEKEGRLPDYIVACVSGGSNAAGIFYPFIDSGVKLIGVEAGGEGLETGKHAASLLKGKIGYLHGSKTFVLQDDWGQVQVSHSVSAGLDYSGVGPEHAYWRETGKVLYDAVTDEEALDAFIELSRLEGIIPALESSHALAYLKKINIKGKVVVVNLSGRGDKDLESVLNHPYVRERIRLEHHHHHH', label_columns=<factory>, primary_label_column='label')[source]#

Bases: BaseCleanerConfig

Configuration class for TrpB dataset cleaner. Inherits from BaseCleanerConfig and adds TrpB-specific configuration options. Simply run mutcleaner.download_trpb_source_file() to download the dataset.

Alternatively, the raw TrpB file can be obtained from:

Attributes:
column_mappingDict[str, str]

Mapping from source to target column names

filtersDict[str, Any]

Filter conditions for data cleaning

type_conversionsDict[str, str]

Data type conversion specifications

is_zero_basedbool

Whether mutation positions are zero-based

validation_workersint

Number of workers for mutation validation

infer_wt_workersint

Number of workers for wildtype sequence inference

handle_multiple_wtLiteral[“error”, “first”, “separate”]

Strategy for handling multiple wildtype sequences

label_columnsList[str]

List of score columns to process

primary_label_columnstr

Primary score column for the dataset

Methods

from_dict(config_dict)

Create configuration object from dictionary

from_json(json_path)

Load configuration from JSON file

get_summary()

Get a human-readable summary of the configuration

merge(partial_config)

Merge partial configuration with current configuration

to_dict([exclude_callables])

Convert configuration to dictionary

to_json(json_path, **json_kwargs)

Save configuration to JSON file

validate()

Validate TrpB-specific configuration parameters
column_mapping: Dict[str, str]#
filters: Dict[str, Callable]#
label_columns: List[str]#
num_workers: int = 16#
pipeline_name: str = 'TrpB Cleaning Pipeline'#
primary_label_column: str = 'label'#
type_conversions: Dict[str, str]#
validate()[source]#

Validate TrpB-specific configuration parameters

Raises:

ValueError – If configuration is invalid

Return type:

None

wt_sequence: str = 'MKGYFGPYGGQYVPEILMGALEELEAAYEGIMKDESFWKEFNDLLRDYAGRPTPLYFARRLSEKYGARVYLKREDLLHTGAHKINNAIGQVLLAKLMGKTRIIAETGAGQHGVATATAAALFGMECVIYMGEEDTIRQKLNVERMKLLGAKVVPVKSGSRTLKDAIDEALRDWITNLQTTYYVFGSVVGPHPYPIIVRNFQKVIGEETKKQIPEKEGRLPDYIVACVSGGSNAAGIFYPFIDSGVKLIGVEAGGEGLETGKHAASLLKGKIGYLHGSKTFVLQDDWGQVQVSHSVSAGLDYSGVGPEHAYWRETGKVLYDAVTDEEALDAFIELSRLEGIIPALESSHALAYLKKINIKGKVVVVNLSGRGDKDLESVLNHPYVRERIRLEHHHHHH'#
mutcleaner.cleaners.trpb_cleaner.clean_trpb_dataset(pipeline)[source]#

Clean TrpB dataset using configurable pipeline

Parameters:

pipeline (Pipeline) – TrpB dataset cleaning pipeline

Return type:

Tuple[Pipeline, MutationDataset]

Returns:

  • Pipeline: The cleaned pipeline - MutationDataset: The cleaned TrpB dataset

Examples

>>> pipeline = create_TrpB_cleaner(df)  # df is raw TrpB dataset file
Use default configuration:

>>> pipeline, dataset = clean_TrpB_dataset(pipeline)

Use partial configuration:

>>> pipeline, dataset = clean_TrpB_dataset(df, config={
...     "validate_mut_workers": 8,
... })

Load configuration from file:

>>> pipeline, dataset = clean_TrpB_dataset(df, config="config.json")
mutcleaner.cleaners.trpb_cleaner.create_trpb_cleaner(dataset_or_path, config=None)[source]#

Create TrpB dataset cleaning pipeline

Parameters:

  • dataset_or_path (Union[str, Path]) – Raw dataset DataFrame or file path to TrpB dataset.

  • config (Union[TrpBCleanerConfig, Dict[str, Any], str, Path, None]) – Configuration for the cleaning pipeline. Can be: - TrpBCleanerConfig object - Dictionary with configuration parameters (merged with defaults) - Path to JSON configuration file (str or Path) - None (uses default configuration)

Return type:

Pipeline

Returns:

Pipeline: The cleaning pipeline used

Raises:

  • TypeError – If config has invalid type

  • ValueError – If configuration validation fails