# mutcleaner/cleaners/cdna_proteolysis_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,
validate_mutations,
average_labels_by_name,
convert_to_mutation_dataset_format,
replace_in_column,
)
from .cdna_proteolysis_custom_cleaners import (
validate_wt_sequence,
)
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__ = [
"CDNAProteolysisCleanerConfig",
"create_cdna_proteolysis_cleaner",
"clean_cdna_proteolysis_dataset",
]
def __dir__() -> List[str]:
return __all__
# Create module logger
logger = logging.getLogger(__name__)
[docs]
@dataclass
class CDNAProteolysisCleanerConfig(BaseCleanerConfig):
"""
Configuration class for cDNAProteolysis dataset cleaner.
Inherits from BaseCleanerConfig and adds cDNAProteolysis-specific configuration options.
Simply run `mutcleaner.download_cdna_proteolysis_source_file()` to download the dataset.
Alternatively, the raw cDNAProteolysis file can be obtained from:
- Hugging Face: https://huggingface.co/datasets/xulab-research/MutCleaner/blob/main/cDNA_Proteolysis_Dataset/Tsuboyama2023_Dataset2_Dataset3_20230416.csv
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
validate_mut_workers : int
Number of workers for mutation validation, set to -1 to use all available CPUs
validate_wt_workers : int
Number of workers for wildtype sequence validation, 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: {
"WT_name": "name",
"aa_seq": "mut_seq",
"mut_type": "mut_info",
"ddG_ML": "label_cDNAProteolysis",
}
)
# Data filtering configuration
filters: Dict[str, Callable] = field(default_factory=lambda: {"label_cDNAProteolysis": lambda s: pd.to_numeric(s, errors="coerce").notna()})
# Type conversion configuration
type_conversions: Dict[str, str] = field(default_factory=lambda: {"label_cDNAProteolysis": "float"})
# Mutation validation parameters
validate_mut_workers: int = 16
# Wildtype validation parameters
validate_wt_workers: int = 16
# Score columns configuration
label_columns: List[str] = field(default_factory=lambda: ["label_cDNAProteolysis"])
primary_label_column: str = "label_cDNAProteolysis"
# Override default pipeline name
pipeline_name: str = "cDNAProteolysis Pipeline"
[docs]
def validate(self) -> None:
"""Validate cDNAProteolysis-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 = {"WT_name", "aa_seq", "mut_type"}
missing = required_mappings - set(self.column_mapping.keys())
if missing:
raise ValueError(f"Missing required column mappings: {missing}")
[docs]
def create_cdna_proteolysis_cleaner(
dataset_or_path: Optional[Union[pd.DataFrame, str, Path]],
config: Optional[Union[CDNAProteolysisCleanerConfig, Dict[str, Any], str, Path]] = None,
) -> Pipeline:
"""Create cDNAProteolysis dataset cleaning pipeline
Parameters
----------
dataset_or_path : Optional[Union[pd.DataFrame, str, Path]]
Raw dataset DataFrame or file path to cDNAProteolysis dataset.
config : Optional[Union[CDNAProteolysisCleanerConfig, Dict[str, Any], str, Path]]
Configuration for the cleaning pipeline. Can be:
- CDNAProteolysisCleanerConfig 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 used
Raises
------
TypeError
If config has invalid type
ValueError
If configuration validation fails
"""
# Handle configuration parameter
if config is None:
final_config = CDNAProteolysisCleanerConfig()
elif isinstance(config, CDNAProteolysisCleanerConfig):
final_config = config
elif isinstance(config, dict):
# Partial configuration - merge with defaults
default_config = CDNAProteolysisCleanerConfig()
final_config = default_config.merge(config)
elif isinstance(config, (str, Path)):
# Load from file
final_config = CDNAProteolysisCleanerConfig.from_json(config)
else:
raise TypeError(f"config must be CDNAProteolysisCleanerConfig, dict, str, Path or None, got {type(config)}")
# Log configuration summary
logger.info(f"cDNAProteolysis dataset will clean 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(
validate_mutations,
mutation_column=final_config.column_mapping.get("mut_type", "mut_type"),
mutation_sep=":",
is_zero_based=False,
exclude_patterns=["wt"],
num_workers=final_config.validate_mut_workers,
)
.delayed_then(
average_labels_by_name,
name_columns=(
final_config.column_mapping.get("WT_name", "WT_name"),
final_config.column_mapping.get("mut_type", "mut_type"),
),
label_columns=final_config.label_columns,
)
.delayed_then(
validate_wt_sequence,
name_column=final_config.column_mapping.get("WT_name", "WT_name"),
mutation_column=final_config.column_mapping.get("mut_type", "mut_type"),
sequence_column=final_config.column_mapping.get("aa_seq", "aa_seq"),
wt_identifier="wt",
num_workers=final_config.validate_wt_workers,
)
.delayed_then(
replace_in_column,
name_column=final_config.column_mapping.get("WT_name", "WT_name"),
old=".pdb",
)
.delayed_then(
convert_to_mutation_dataset_format,
name_column=final_config.column_mapping.get("WT_name", "WT_name"),
mutation_column=final_config.column_mapping.get("mut_type", "mut_type"),
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)
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 cDNAProteolysis cleaning pipeline: {str(e)}")
raise RuntimeError(f"Error in creating cDNAProteolysis cleaning pipeline: {str(e)}")
[docs]
def clean_cdna_proteolysis_dataset(
pipeline: Pipeline,
) -> Tuple[Pipeline, MutationDataset]:
"""Clean cDNAProteolysis dataset using configurable pipeline
Parameters
----------
pipeline : Pipeline
cDNAProteolysis dataset cleaning pipeline
Returns
-------
Tuple[Pipeline, MutationDataset]
- Pipeline: The cleaned pipeline
- MutationDataset: The cleaned cDNAProteolysis dataset
Examples
--------
Use default configuration:
>>> pipeline = create_cdna_proteolysis_cleaner(df) # df is raw cDNAProteolysis dataset file
Use partial configuration:
>>> pipeline = create_cdna_proteolysis_cleaner(df, config={
... "validate_mut_workers": 8,
... })
Load configuration from file:
>>> pipeline = create_cdna_proteolysis_cleaner(df, config="config.json")
>>> pipeline, dataset = clean_cdna_proteolysis_dataset(pipeline)
"""
try:
# Run pipeline
pipeline.execute()
# Extract results
cdna_proteolysis_dataset_df, cdna_proteolysis_ref_seq = pipeline.data
cdna_proteolysis_dataset = MutationDataset.from_dataframe(cdna_proteolysis_dataset_df, cdna_proteolysis_ref_seq)
logger.info(f"Successfully cleaned cDNAProteolysis dataset:{len(cdna_proteolysis_dataset_df)} mutations from {len(cdna_proteolysis_ref_seq)} proteins")
return pipeline, cdna_proteolysis_dataset
except Exception as e:
logger.error(f"Error in running cDNAProteolysis dataset cleaning pipeline: {str(e)}")
raise RuntimeError(f"Error in running cDNAProteolysis dataset cleaning pipeline: {str(e)}")