tidymut.cleaners.human_domainome_cleaner module
- class tidymut.cleaners.human_domainome_cleaner.HumanDomainomeCleanerConfig(num_workers: int = 16, validate_config: bool = True, *, pipeline_name: str = 'human_domainome_cleaner', sequence_dict_path: Union[str, Path], header_parser: Callable[[str], Tuple[str, Dict[str, str]]] = <function parse_uniprot_header>, column_mapping: Dict[str, str] = <factory>, type_conversions: Dict[str, str] = <factory>, drop_na_columns: List = <factory>, is_zero_based: bool = False, process_workers: int = 16, label_columns: List[str] = <factory>, primary_label_column: str = 'label_humanDomainome')[source]
Bases:
BaseCleanerConfig
Configuration class for HumanDomainome dataset cleaner
Inherits from BaseCleanerConfig and adds HumanDomainome-specific configuration options.
- sequence_dict_path
Path to the file containing UniProt ID to sequence mapping
- Type:
Union[str, Path]
- header_parser
Parse Header in fasta files and extract relevant information
- Type:
Callable[[str], Tuple[str, Dict[str, str]]]
- column_mapping
Mapping from source to target column names
- Type:
Dict[str, str]
- type_conversions
Data type conversion specifications
- Type:
Dict[str, str]
- drop_na_columns
List of column names where null values should be dropped
- Type:
List[str]
- is_zero_based
Whether mutation positions are zero-based
- Type:
bool
- process_workers
Number of workers for parallel processing
- Type:
int
- label_columns
List of score columns to process
- Type:
List[str]
- primary_label_column
Primary score column for the dataset
- Type:
str
- column_mapping: Dict[str, str]
- drop_na_columns: List
- header_parser() Tuple[str, Dict[str, str]]
Parse UniProt FASTA header to extract ID and metadata
- Parameters:
header (str) – FASTA header line (without ‘>’)
- Returns:
(sequence_id, metadata_dict)
- Return type:
Tuple[str, Dict[str, str]]
Examples
>>> parse_uniprot_header("sp|P12345|PROT_HUMAN Protein description OS=Homo sapiens") ('P12345', {'db': 'sp', 'entry_name': 'PROT_HUMAN', 'description': 'Protein description OS=Homo sapiens'}) >>> parse_uniprot_header("P12345|PROT_HUMAN Description") ('P12345', {'entry_name': 'PROT_HUMAN', 'description': 'Description'}) >>> parse_uniprot_header("P12345") ('P12345', {})
- is_zero_based: bool = False
- label_columns: List[str]
- pipeline_name: str = 'human_domainome_cleaner'
- primary_label_column: str = 'label_humanDomainome'
- process_workers: int = 16
- sequence_dict_path: str | Path
- type_conversions: Dict[str, str]
- tidymut.cleaners.human_domainome_cleaner.clean_human_domainome_dataset(pipeline: Pipeline) Tuple[Pipeline, MutationDataset] [source]
Clean HumanDomainome dataset using configurable pipeline
- Parameters:
pipeline (Pipeline) – HumanDomainome dataset cleaning pipeline
- Returns:
Pipeline: The cleaned pipeline
MutationDataset: The cleaned HumanDomainome dataset
- Return type:
Tuple[Pipeline, MutationDataset]
- Raises:
RuntimeError – If pipeline execution fails
- tidymut.cleaners.human_domainome_cleaner.create_human_domainome_cleaner(dataset_or_path: str | Path, sequence_dict_path: str | Path, config: HumanDomainomeCleanerConfig | Dict[str, Any] | str | Path | None = None) Pipeline [source]
Create HumanDomainome dataset cleaning pipeline
- Parameters:
dataset_or_path (Union[pd.DataFrame, str, Path]) –
Raw HumanDomainome dataset DataFrame or file path to K50 HumanDomainome - File: SupplementaryTable4.txt from the article
’Site-saturation mutagenesis of 500 human protein domains’
sequence_dict_path (Union[str, Path]) – Path to file containing UniProt ID to sequence mapping
config (Optional[Union[HumanDomainomeCleanerConfig, Dict[str, Any], str, Path]]) – Configuration for the cleaning pipeline. Can be: - HumanDomainomeCleanerConfig object - Dictionary with configuration parameters (merged with defaults) - Path to JSON configuration file (str or Path) - None (uses default configuration)
- Returns:
The cleaning pipeline
- Return type:
- Raises:
FileNotFoundError – If data file or sequence dictionary file not found
TypeError – If config has invalid type
ValueError – If configuration validation fails
Examples
Basic usage: >>> pipeline = create_human_domainome_cleaner( … “human_domainome.csv”, … “uniprot_sequences.fasta” … ) >>> pipeline, dataset = clean_human_domainome_dataset(pipeline)
Custom configuration: >>> config = { … “process_workers”: 8, … “type_conversions”: {“label_humanDomainome”: “float32”} … } >>> pipeline = create_human_domainome_cleaner( … “human_domainome.csv”, … “sequences.csv”, … config=config … )
Load configuration from file: >>> pipeline = create_human_domainome_cleaner( … “data.csv”, … “sequences.fasta”, … config=”config.json” … )