# mutcleaner/cleaners/antitoxin_pard3_cleaner.py
from __future__ import annotations
import pandas as pd
from typing import TYPE_CHECKING
from dataclasses import dataclass, field
from pathlib import Path
import logging
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,
add_columns,
apply_mutations_to_sequences,
average_labels_by_name,
subtract_labels_by_wt,
)
from .antitoxin_pard3_custom_cleaners import (
simplify_mutations,
)
from ..core.dataset import MutationDataset
from ..core.pipeline import Pipeline, create_pipeline
if TYPE_CHECKING:
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
__all__ = [
"AntitoxinParD3CleanerConfig",
"create_antitoxin_pard3_cleaner",
"clean_antitoxin_pard3_dataset",
]
def __dir__() -> List[str]:
return __all__
# Create module logger
logger = logging.getLogger(__name__)
[docs]
@dataclass
class AntitoxinParD3CleanerConfig(BaseCleanerConfig):
"""
Configuration class for Antitoxin dataset cleaner.
Inherits from BaseCleanerConfig and adds Antitoxin-specific configuration options.
Simply run `mutcleaner.download_antitoxin_source_file()` to download the dataset.
Alternatively, the raw Antitoxin file can be obtained from:
- Hugging Face: https://huggingface.co/datasets/xulab-research/MutCleaner/blob/main/Antitoxin_ParD3_Epistasis_Dataset/Antitoxin_ParD3_Epistasis_Dataset.csv
Attributes
----------
column_mapping : Dict[str, str]
Mapping from source to target column names
filters : Dict[str, Callable]
Filter conditions for data cleaning
wt_sequence : str
Wildtype sequence for the dataset, used for mutation validation
type_conversions : Dict[str, str]
Data type conversion specifications
validate_mut_workers : int
Number of workers for mutation validation, set to -1 to use all available CPUs
process_workers : int
Number of workers for applying mutations to sequences, 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
pipeline_name : str
Name of the cleaning pipeline
"""
# Column mapping configuration
column_mapping: Dict[str, str] = field(
default_factory=lambda: {
"mutation": "mut_info",
"label": "label",
}
)
# Data filtering configuration
filters: Dict[str, Callable] = field(
default_factory=lambda: {
"label": lambda s: pd.to_numeric(s, errors="coerce").notna()
}
)
# obtained from the article
wt_sequence = "MANVEKMSVAVTPQQAAVMREAVEAGEYATASEIVREAVRDWLAKRELRHDDIRRLRQLWDEGKASGRPEPVDFDALRKEARQKLTEVPPNGR"
# Type conversion configuration
type_conversions: Dict[str, str] = field(default_factory=lambda: {"label": "float"})
# Mutation validation parameters
validate_mut_workers: int = 16
process_workers: int = 16
# Score columns configuration
label_columns: List[str] = field(default_factory=lambda: ["label"])
primary_label_column: str = "label"
# Override default pipeline name
pipeline_name: str = "Antitoxin Pipeline"
[docs]
def validate(self) -> None:
"""Validate Antitoxin-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 = {"mutation"}
missing = required_mappings - set(self.column_mapping.keys())
if missing:
raise ValueError(f"Missing required column mappings: {missing}")
[docs]
def create_antitoxin_pard3_cleaner(
dataset_or_path: Optional[Union[pd.DataFrame, str, Path]] = None,
config: Optional[
Union[AntitoxinParD3CleanerConfig, Dict[str, Any], str, Path]
] = None,
) -> Pipeline:
"""Create Antitoxin dataset cleaning pipeline
Parameters
----------
dataset_or_path : Optional[Union[pd.DataFrame, str, Path]], default=None
Raw dataset DataFrame or file path to Antitoxin dataset.
config : Optional[Union[AntitoxinCleanerConfig, Dict[str, Any], str, Path]]
Configuration for the cleaning pipeline. Can be:
- AntitoxinCleanerConfig object
- Dictionary with configuration parameters (merged with defaults)
- Path to JSON configuration file (str or Path)
- None (uses default configuration)
Returns
-------
Pipeline
Pipeline: The cleaning pipeline used
Raises
------
TypeError
If config has invalid type
ValueError
If configuration validation fails
"""
# Handle configuration parameter
if config is None:
final_config = AntitoxinParD3CleanerConfig()
elif isinstance(config, AntitoxinParD3CleanerConfig):
final_config = config
elif isinstance(config, dict):
# Partial configuration - merge with defaults
default_config = AntitoxinParD3CleanerConfig()
final_config = default_config.merge(config)
elif isinstance(config, (str, Path)):
# Load from file
final_config = AntitoxinParD3CleanerConfig.from_json(config)
else:
raise TypeError(
f"config must be AntitoxinParD3CleanerConfig, dict, str, Path or None, got {type(config)}"
)
# Log configuration summary
logger.info(
f"Antitoxin dataset 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,
)
.delayed_then(
convert_data_types,
type_conversions=final_config.type_conversions,
)
.delayed_then(
add_columns,
columns_to_add={
"name": "antitoxin",
"wt_seq": final_config.wt_sequence,
},
)
.delayed_then(
simplify_mutations,
mutation_column=final_config.column_mapping.get("mutation", "mutation"),
mutation_sep=":",
)
.delayed_then(
validate_mutations,
mutation_column=final_config.column_mapping.get("mutation", "mutation"),
mutation_sep=",",
is_zero_based=True,
exclude_patterns="WT",
num_workers=final_config.validate_mut_workers,
)
.delayed_then(
average_labels_by_name,
name_columns=(
"name",
final_config.column_mapping.get("mutation", "mutation"),
),
label_columns=final_config.primary_label_column,
)
.delayed_then(
subtract_labels_by_wt,
name_column="name",
label_columns=final_config.primary_label_column,
mutation_column=final_config.column_mapping.get("mutation", "mutation"),
wt_identifier="WT",
in_place=True,
)
.delayed_then(
apply_mutations_to_sequences,
sequence_column="wt_seq",
name_column="name",
mutation_column=final_config.column_mapping.get("mutation", "mutation"),
is_zero_based=True,
sequence_type="protein",
num_workers=final_config.process_workers,
)
.delayed_then(
convert_to_mutation_dataset_format,
name_column="name",
mutation_column=final_config.column_mapping.get("mutation", "mutation"),
sequence_column="wt_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="csv")
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 aav capsid cleaning pipeline: {str(e)}")
raise RuntimeError(f"Error in creating aav capsid cleaning pipeline: {str(e)}")
[docs]
def clean_antitoxin_pard3_dataset(
pipeline: Pipeline,
) -> Tuple[Pipeline, MutationDataset]:
"""Clean Antitoxin dataset using configurable pipeline
Parameters
----------
pipeline : Pipeline
Antitoxin dataset cleaning pipeline
Returns
-------
Tuple[Pipeline, MutationDataset]
- Pipeline: The cleaned pipeline
- MutationDataset: The cleaned Antitoxin dataset
Examples
--------
Use default configuration:
>>> pipeline = create_antitoxin_cleaner(df) # df is raw Antitoxin dataset file
Use partial configuration:
>>> pipeline = create_antitoxin_cleaner(df, config={
... "validate_mut_workers": 8,
... })
Load configuration from file:
>>> pipeline = create_antitoxin_cleaner(df, config="config.json")
>>> pipeline, dataset = clean_antitoxin_dataset(pipeline)
"""
try:
# Run pipeline
pipeline.execute()
# Extract results
antitoxin_dataset_df, antitoxin_ref_seq = pipeline.data
antitoxin_dataset = MutationDataset.from_dataframe(
antitoxin_dataset_df, antitoxin_ref_seq
)
logger.info(
f"Successfully cleaned antitoxin dataset: {len(antitoxin_dataset_df)} mutations from {len(antitoxin_ref_seq)} proteins"
)
return pipeline, antitoxin_dataset
except Exception as e:
logger.error(f"Error in running antitoxin dataset cleaning pipeline: {str(e)}")
raise RuntimeError(
f"Error in running antitoxin dataset cleaning pipeline: {str(e)}"
)