# mutcleaner/cleaners/human_domainome_sup2_cleaner.py
from __future__ import annotations
import logging
import pandas as pd
from typing import TYPE_CHECKING
from dataclasses import dataclass, field
from pathlib import Path
from .base_config import BaseCleanerConfig
from .basic_cleaners import (
read_dataset,
extract_and_rename_columns,
filter_and_clean_data,
convert_data_types,
convert_to_mutation_dataset_format,
validate_mutations,
infer_wildtype_sequences,
)
from .human_domainome_custom_cleaners import generate_mutation_strings
from ..core.dataset import MutationDataset
from ..core.pipeline import Pipeline, create_pipeline
if TYPE_CHECKING:
from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
__all__ = [
"HumanDomainomeSup2CleanerConfig",
"create_human_domainome_sup2_cleaner",
"clean_human_domainome_sup2_dataset",
]
def __dir__() -> List[str]:
return __all__
# Create module logger
logger = logging.getLogger(__name__)
[docs]
@dataclass(kw_only=True)
class HumanDomainomeSup2CleanerConfig(BaseCleanerConfig):
"""
Configuration class for HumanDomainome dataset cleaner - SupplementaryTable2.
Inherits from BaseCleanerConfig and adds HumanDomainome-specific configuration options.
Simply run `mutcleaner.download_human_domainome_source_file()` to download the dataset.
Alternatively, the raw HumanDomainome file and the wild type fasta file can be obtained from:
- Hugging Face: https://huggingface.co/datasets/xulab-research/MutCleaner/blob/main/Human_Domainome_Dataset/SupplementaryTable2.txt
- Hugging Face: https://huggingface.co/datasets/xulab-research/MutCleaner/blob/main/Human_Domainome_Dataset/wild_type.fasta
Attributes
----------
column_mapping : Dict[str, str]
Mapping from source to target column names
filters : Dict[str, Callable]
Filter conditions for data cleaning
type_conversions : Dict[str, str]
Data type conversion specifications
drop_na_columns: List[str]
List of column names where null values should be dropped
validation_workers : int
Number of workers for mutations validation, set to -1 to use all available CPUs
infer_wt_workers : int
Number of workers for wildtype sequences inference, set to -1 to use all available CPUs
label_columns : List[str]
List of score columns to process
primary_label_column : str
Primary score column for the dataset
"""
# Column mapping configuration
column_mapping: Dict[str, str] = field(
default_factory=lambda: {
"domain_ID": "name",
"aa_seq": "mut_seq",
"wt_aa": "wt_aa",
"mut_aa": "mut_aa",
"position": "pos",
"normalized_fitness": "label_humanDomainome",
}
)
# Exclude nonsense mutations by default
filters: Dict[str, Callable] = field(
default_factory=lambda: {"mut_aa": lambda x: x != "*"}
)
# columns to perfrom dropping NA
drop_na_columns: List = field(
default_factory=lambda: [
"name",
"mut_seq",
"wt_aa",
"mut_aa",
"pos",
"label_humanDomainome",
]
)
# Type conversion configuration
type_conversions: Dict[str, str] = field(
default_factory=lambda: {"label_humanDomainome": "float"}
)
# Mutation validation parameters
validation_workers: int = 16
# Wildtype inference parameters
infer_wt_workers: int = 16
handle_multiple_wt: Literal["error", "first", "separate"] = "error"
# Score columns configuration
label_columns: List[str] = field(default_factory=lambda: ["label_humanDomainome"])
primary_label_column: str = "label_humanDomainome"
# Override default pipeline name
pipeline_name: str = "human_domainome_cleaner"
def __post_init__(self):
self.type_conversions.update({"pos": "int", "mut_rel_pos": "int"})
return super().__post_init__()
[docs]
def validate(self) -> None:
"""Validate HumanDomainome-specific configuration parameters
Raises
------
ValueError
If configuration is invalid
"""
# Call parent validation
super().validate()
# Validate score columns
if not self.label_columns:
raise ValueError("label_columns cannot be empty")
if self.primary_label_column not in self.label_columns:
raise ValueError(
f"primary_label_column '{self.primary_label_column}' must be in label_columns {self.label_columns}"
)
# Validate column mapping
required_mappings = set(self.column_mapping.keys())
missing = required_mappings - set(self.column_mapping.keys())
if missing:
raise ValueError(f"Missing required column mappings: {missing}")
[docs]
def create_human_domainome_sup2_cleaner(
dataset_or_path: Union[str, Path, pd.DataFrame],
config: Optional[
Union[HumanDomainomeSup2CleanerConfig, Dict[str, Any], str, Path]
] = None,
) -> Pipeline:
"""Create HumanDomainome ledataset cleaning pipeline - SupplementaryTable2
Parameters
----------
dataset_or_path : Union[pd.DataFrame, str, Path]
Raw HumanDomainome dataset DataFrame or file path to HumanDomainome
- File: `SupplementaryTable2.txt` from the article
'Site-saturation mutagenesis of 500 human protein domains'
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
-------
Pipeline
The cleaning pipeline
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_sup2_cleaner(
... "human_domainome.csv"
... )
>>> pipeline, dataset = clean_human_domainome_dataset(pipeline)
Custom configuration:
>>> config = {
... "process_workers": 8,
... "type_conversions": {"label_humanDomainome": "float32"}
... }
>>> pipeline = create_human_domainome_sup2_cleaner(
... "human_domainome.csv"
... config=config
... )
Load configuration from file:
>>> pipeline = create_human_domainome_sup2_cleaner(
... "data.csv",
... config="config.json"
... )
"""
# Handle configuration parameter
if config is None:
final_config = HumanDomainomeSup2CleanerConfig()
elif isinstance(config, HumanDomainomeSup2CleanerConfig):
final_config = config
elif isinstance(config, dict):
# Partial configuration - merge with defaults
default_config = HumanDomainomeSup2CleanerConfig()
final_config = default_config.merge(config)
elif isinstance(config, (str, Path)):
# Load from file
final_config = HumanDomainomeSup2CleanerConfig.from_json(config)
else:
raise TypeError(
f"config must be HumanDomainomeSup2CleanerConfig, dict, str, Path or None, got {type(config)}"
)
# Log configuration summary
logger.info(
f"HumanDomainome dataset (SupplementaryTable2) will be cleaned with pipeline: {final_config.pipeline_name}"
)
logger.debug(f"Configuration:\n{final_config.get_summary()}")
try:
# Create pipeline
pipeline = create_pipeline(dataset_or_path, final_config.pipeline_name)
# Add cleaning steps
pipeline = (
pipeline.delayed_then(
extract_and_rename_columns,
column_mapping=final_config.column_mapping,
)
.delayed_then(
filter_and_clean_data,
filters=final_config.filters,
drop_na_columns=final_config.drop_na_columns,
)
.delayed_then(
convert_data_types,
type_conversions=final_config.type_conversions,
)
.delayed_then(
generate_mutation_strings,
name_column=final_config.column_mapping.get("domain_ID", "domain_ID"),
wt_aa_column=final_config.column_mapping.get("wt_aa", "wt_aa"),
mut_aa_column=final_config.column_mapping.get("mut_aa", "mut_aa"),
aa_pos_column=final_config.column_mapping.get("position", "pos"),
)
.delayed_then(
validate_mutations,
mutation_column="mut_info",
format_mutations=False, # Formatting is not needed after generate_mutation_strings
is_zero_based=True,
num_workers=final_config.validation_workers,
)
.delayed_then(
infer_wildtype_sequences,
name_column=final_config.column_mapping.get("domain_ID", "domain_ID"),
mutation_column="mut_info",
sequence_column=final_config.column_mapping.get("aa_seq", "aa_seq"),
label_columns=final_config.label_columns,
handle_multiple_wt=final_config.handle_multiple_wt,
is_zero_based=True, # Always True after validate_mutations
num_workers=final_config.infer_wt_workers,
)
.delayed_then(
convert_to_mutation_dataset_format,
name_column=final_config.column_mapping.get("domain_ID", "domain_ID"),
mutation_column="mut_info",
mutated_sequence_column=final_config.column_mapping.get(
"aa_seq", "aa_seq"
),
label_column=final_config.primary_label_column,
is_zero_based=True,
)
)
# Create pipeline based on dataset_or_path type
if isinstance(dataset_or_path, (str, Path)):
pipeline.add_delayed_step(read_dataset, 0, file_format="tsv")
elif not isinstance(dataset_or_path, pd.DataFrame):
raise TypeError(
f"dataset_or_path must be pd.DataFrame or str/Path, got {type(dataset_or_path)}"
)
return pipeline
except Exception as e:
logger.error(f"Error in creating HumanDomainome cleaning pipeline: {str(e)}")
raise RuntimeError(
f"Error in creating HumanDomainome cleaning pipeline: {str(e)}"
)
[docs]
def clean_human_domainome_sup2_dataset(
pipeline: Pipeline,
) -> Tuple[Pipeline, MutationDataset]:
"""Clean HumanDomainome dataset using configurable pipeline - SupplementaryTable2
Parameters
----------
pipeline : Pipeline
HumanDomainome dataset cleaning pipeline
Returns
-------
Tuple[Pipeline, MutationDataset]
- Pipeline: The cleaned pipeline
- MutationDataset: The cleaned HumanDomainome dataset
Raises
------
RuntimeError
If pipeline execution fails
"""
try:
# Run pipeline
pipeline.execute()
# Extract results
dataset_df, ref_sequences = pipeline.data
human_domainome_dataset = MutationDataset.from_dataframe(
dataset_df, ref_sequences
)
logger.info(
f"Successfully cleaned HumanDomainome dataset: {len(dataset_df)} mutations from {len(ref_sequences)} proteins"
)
return pipeline, human_domainome_dataset
except Exception as e:
logger.error(f"Error in running HumanDomainome cleaning pipeline: {str(e)}")
raise RuntimeError(
f"Error in running HumanDomainome cleaning pipeline: {str(e)}"
)