comparison COBRAxy/ras_to_bounds.py @ 489:97eea560a10f draft

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488:e0bcc61b2feb

import argparse
import utils.general_utils as utils
from typing import Optional, List
import os
import numpy as np
import pandas as pd
import cobra
import sys
import csv
from joblib import Parallel, delayed, cpu_count

################################# process args ###############################
def process_args(args :List[str] = None) -> argparse.Namespace:
    """
    Processes command-line arguments.

        Namespace: An object containing parsed arguments.
    """
    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
                                     description = 'process some value\'s')
    
    parser.add_argument(
        '-ms', '--model_selector', 
        type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
        help = 'chose which type of model you want use')
    
    parser.add_argument("-mo", "--model", type = str,
        help = "path to input file with custom rules, if provided")
    
    parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")

    parser.add_argument(
        '-mes', '--medium_selector', 
        default = "allOpen",
        help = 'chose which type of medium you want use')
    
    parser.add_argument("-meo", "--medium", type = str,
        help = "path to input file with custom medium, if provided")

    parser.add_argument('-ol', '--out_log', 
                        help = "Output log")
    
    parser.add_argument('-td', '--tool_dir',

                        help = 'input ras')
    
    parser.add_argument('-rn', '--name',
                type=str,
                help = 'ras class names')
    
    parser.add_argument('-rs', '--ras_selector',
                        required = True,
                        type=utils.Bool("using_RAS"),
                        help = 'ras selector')

    parser.add_argument('-cc', '--cell_class',
                    type = str,
                    help = 'output of cell class')
    parser.add_argument(
        '-idop', '--output_path', 
        type = str,
        default='ras_to_bounds/',
        help = 'output path for maps')
    
    
    ARGS = parser.parse_args(args)
    return ARGS

########################### warning ###########################################

        s (str): The warning message to be logged and printed.
    
    Returns:
      None
    """
    with open(ARGS.out_log, 'a') as log:
        log.write(s + "\n\n")
    print(s)

############################ dataset input ####################################
def read_dataset(data :str, name :str) -> pd.DataFrame:
    """

            if upper_bound!=0 and lower_bound!=0:
                new_bounds.loc[reaction, "lower_bound"] = valMin
                new_bounds.loc[reaction, "upper_bound"] = valMax
    return new_bounds

def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
    """
    Process a single RAS cell, apply bounds, and save the bounds to a CSV file.

    Args:
        cellName (str): The name of the RAS cell (used for naming the output file).
        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
        model (cobra.Model): The metabolic model to be modified.
        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
        output_folder (str): Folder path where the output CSV file will be saved.
    
    Returns:
        None
    """
    bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
    new_bounds = apply_ras_bounds(bounds, ras_row)
    new_bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
    pass

def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
    """
    Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
    
    Args:
        model (cobra.Model): The metabolic model for which bounds will be generated.
        medium (dict): A dictionary where keys are reaction IDs and values are the medium conditions.
        ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
        output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.

    Returns:
        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
    """
    rxns_ids = [rxn.id for rxn in model.reactions]

    # Set all reactions to zero in the medium
    for rxn_id, _ in model.medium.items():
        model.reactions.get_by_id(rxn_id).lower_bound = float(0.0)
    
    # Set medium conditions
    for reaction, value in medium.items():
        if value is not None:
            model.reactions.get_by_id(reaction).lower_bound = -float(value)
            
            
    # Perform Flux Variability Analysis (FVA) on this medium
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds
    for reaction in rxns_ids:
        model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
        model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])

    if ras is not None:
        Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
    else:
        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
        newBounds = apply_ras_bounds(bounds, pd.Series([1]*len(rxns_ids), index=rxns_ids))
        newBounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
    pass



############################# main ###########################################
def main(args:List[str] = None) -> None:
    """
    Initializes everything and sets the program in motion based on the fronted input arguments.

    Returns:
        None
    """
    if not os.path.exists('ras_to_bounds'):
        os.makedirs('ras_to_bounds')


    global ARGS
    ARGS = process_args(args)

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        ras_file_names = ARGS.name.split(",")
        if len(ras_file_names) != len(set(ras_file_names)):
            error_message = "Duplicated file names in the uploaded RAS matrices."
            warning(error_message)
            raise ValueError(error_message)
            pass
        ras_class_names = []
        for file in ras_file_names:
            ras_class_names.append(file.rsplit(".", 1)[0])
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
        for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
            ras = read_dataset(ras_matrix, "ras dataset")
            ras.replace("None", None, inplace=True)
            ras.set_index("Reactions", drop=True, inplace=True)
            ras = ras.T
            ras = ras.astype(float)
            if(len(ras_file_list)>1):
                #append class name to patient id (dataframe index)
                ras.index = [f"{idx}_{ras_class_name}" for idx in ras.index]
            else:
                ras.index = [f"{idx}" for idx in ras.index]
            ras_list.append(ras)
            for patient_id in ras.index:
                class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
        
        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=0)
        # Normalize the RAS values by max RAS
        ras_combined = ras_combined.div(ras_combined.max(axis=0))
        ras_combined.dropna(axis=1, how='all', inplace=True)


    
    model_type :utils.Model = ARGS.model_selector
    if model_type is utils.Model.Custom:
        model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
    else:
        model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)

    if(ARGS.medium_selector == "Custom"):
        medium = read_dataset(ARGS.medium, "medium dataset")
        medium.set_index(medium.columns[0], inplace=True)
        medium = medium.astype(float)
        medium = medium[medium.columns[0]].to_dict()
    else:
        df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
        ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
        medium = df_mediums[[ARGS.medium_selector]]
        medium = medium[ARGS.medium_selector].to_dict()

    if(ARGS.ras_selector == True):
        generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_path)
        class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False)
    else:
        generate_bounds(model, medium, output_folder=ARGS.output_path)

    pass
        
##############################################################################
if __name__ == "__main__":
    main()

489:97eea560a10f

"""
Apply RAS-based scaling to reaction bounds and optionally save updated models.

Workflow:
- Read one or more RAS matrices (patients/samples x reactions)
- Normalize and merge them, optionally adding class suffixes to sample IDs
- Build a COBRA model from a tabular CSV
- Run FVA to initialize bounds, then scale per-sample based on RAS values
- Save bounds per sample and optionally export updated models in chosen formats
"""
import argparse
import utils.general_utils as utils
from typing import Optional, Dict, Set, List, Tuple, Union
import os
import numpy as np
import pandas as pd
import cobra
from cobra import Model
import sys
from joblib import Parallel, delayed, cpu_count
import utils.model_utils as modelUtils

################################# process args ###############################
def process_args(args :List[str] = None) -> argparse.Namespace:
    """
    Processes command-line arguments.

        Namespace: An object containing parsed arguments.
    """
    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
                                     description = 'process some value\'s')
    
    
    parser.add_argument("-mo", "--model_upload", type = str,
        help = "path to input file with custom rules, if provided")

    parser.add_argument('-ol', '--out_log', 
                        help = "Output log")
    
    parser.add_argument('-td', '--tool_dir',

                        help = 'input ras')
    
    parser.add_argument('-rn', '--name',
                type=str,
                help = 'ras class names')

    parser.add_argument('-cc', '--cell_class',
                    type = str,
                    help = 'output of cell class')
    parser.add_argument(
        '-idop', '--output_path', 
        type = str,
        default='ras_to_bounds/',
        help = 'output path for maps')
    
    parser.add_argument('-sm', '--save_models',
                    type=utils.Bool("save_models"),
                    default=False,
                    help = 'whether to save models with applied bounds')
    
    parser.add_argument('-smp', '--save_models_path',
                        type = str,
                        default='saved_models/',
                        help = 'output path for saved models')
    
    parser.add_argument('-smf', '--save_models_format',
                        type = str,
                        default='csv',
                        help = 'format for saved models (csv, xml, json, mat, yaml, tabular)')

    
    ARGS = parser.parse_args(args)
    return ARGS

########################### warning ###########################################

        s (str): The warning message to be logged and printed.
    
    Returns:
      None
    """
    if ARGS.out_log:
        with open(ARGS.out_log, 'a') as log:
            log.write(s + "\n\n")
    print(s)

############################ dataset input ####################################
def read_dataset(data :str, name :str) -> pd.DataFrame:
    """

            if upper_bound!=0 and lower_bound!=0:
                new_bounds.loc[reaction, "lower_bound"] = valMin
                new_bounds.loc[reaction, "upper_bound"] = valMax
    return new_bounds


def save_model(model, filename, output_folder, file_format='csv'):
    """
    Save a COBRA model to file in the specified format.
    
    Args:
        model (cobra.Model): The model to save.
        filename (str): Base filename (without extension).
        output_folder (str): Output directory.
        file_format (str): File format ('xml', 'json', 'mat', 'yaml', 'tabular', 'csv').
    
    Returns:
        None
    """
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    
    try:
        if file_format == 'tabular' or file_format == 'csv':
            # Special handling for tabular format using utils functions
            filepath = os.path.join(output_folder, f"{filename}.csv")
            
            rules = modelUtils.generate_rules(model, asParsed = False)
            reactions = modelUtils.generate_reactions(model, asParsed = False)
            bounds = modelUtils.generate_bounds(model)
            medium = modelUtils.get_medium(model)
            
            try:
                compartments = modelUtils.generate_compartments(model)
            except:
                compartments = None

            df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
            df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
            df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
            df_medium = medium.rename(columns = {"reaction": "ReactionID"})
            df_medium["InMedium"] = True

            merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
            merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
            
            # Add compartments only if they exist and model name is ENGRO2
            if compartments is not None and hasattr(ARGS, 'name') and ARGS.name == "ENGRO2": 
                merged = merged.merge(compartments, on = "ReactionID", how = "outer")
            
            merged = merged.merge(df_medium, on = "ReactionID", how = "left")
            merged["InMedium"] = merged["InMedium"].fillna(False)
            merged = merged.sort_values(by = "InMedium", ascending = False)
            
            merged.to_csv(filepath, sep="\t", index=False)
            
        else:
            # Standard COBRA formats
            filepath = os.path.join(output_folder, f"{filename}.{file_format}")
            
            if file_format == 'xml':
                cobra.io.write_sbml_model(model, filepath)
            elif file_format == 'json':
                cobra.io.save_json_model(model, filepath)
            elif file_format == 'mat':
                cobra.io.save_matlab_model(model, filepath)
            elif file_format == 'yaml':
                cobra.io.save_yaml_model(model, filepath)
            else:
                raise ValueError(f"Unsupported format: {file_format}")
        
        print(f"Model saved: {filepath}")
        
    except Exception as e:
        warning(f"Error saving model {filename}: {str(e)}")

def apply_bounds_to_model(model, bounds):
    """
    Apply bounds from a DataFrame to a COBRA model.
    
    Args:
        model (cobra.Model): The metabolic model to modify.
        bounds (pd.DataFrame): DataFrame with reaction bounds.
    
    Returns:
        cobra.Model: Modified model with new bounds.
    """
    model_copy = model.copy()
    for reaction_id in bounds.index:
        try:
            reaction = model_copy.reactions.get_by_id(reaction_id)
            reaction.lower_bound = bounds.loc[reaction_id, "lower_bound"]
            reaction.upper_bound = bounds.loc[reaction_id, "upper_bound"]
        except KeyError:
            # Reaction not found in model, skip
            continue
    return model_copy

def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder, save_models=False, save_models_path='saved_models/', save_models_format='csv'):
    """
    Process a single RAS cell, apply bounds, and save the bounds to a CSV file.

    Args:
        cellName (str): The name of the RAS cell (used for naming the output file).
        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
        model (cobra.Model): The metabolic model to be modified.
        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
        output_folder (str): Folder path where the output CSV file will be saved.
        save_models (bool): Whether to save models with applied bounds.
        save_models_path (str): Path where to save models.
        save_models_format (str): Format for saved models.
    
    Returns:
        None
    """
    bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
    new_bounds = apply_ras_bounds(bounds, ras_row)
    new_bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
    
    # Save model if requested
    if save_models:
        modified_model = apply_bounds_to_model(model, new_bounds)
        save_model(modified_model, cellName, save_models_path, save_models_format)
    
    return

def generate_bounds_model(model: cobra.Model, ras=None, output_folder='output/', save_models=False, save_models_path='saved_models/', save_models_format='csv') -> pd.DataFrame:
    """
    Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
    
    Args:
        model (cobra.Model): The metabolic model for which bounds will be generated.
        ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
        output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.
        save_models (bool): Whether to save models with applied bounds.
        save_models_path (str): Path where to save models.
        save_models_format (str): Format for saved models.

    Returns:
        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
    """
    rxns_ids = [rxn.id for rxn in model.reactions]            
            
    # Perform Flux Variability Analysis (FVA) on this medium
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds
    for reaction in rxns_ids:
        model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
        model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])

    if ras is not None:
        Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(
            cellName, ras_row, model, rxns_ids, output_folder, 
            save_models, save_models_path, save_models_format
        ) for cellName, ras_row in ras.iterrows())
    else:
        raise ValueError("RAS DataFrame is None. Cannot generate bounds without RAS data.")
    return

############################# main ###########################################
def main(args:List[str] = None) -> None:
    """
    Initialize and execute RAS-to-bounds pipeline based on the frontend input arguments.

    Returns:
        None
    """
    if not os.path.exists('ras_to_bounds'):
        os.makedirs('ras_to_bounds')

    global ARGS
    ARGS = process_args(args)


    ras_file_list = ARGS.input_ras.split(",")
    ras_file_names = ARGS.name.split(",")
    if len(ras_file_names) != len(set(ras_file_names)):
        error_message = "Duplicated file names in the uploaded RAS matrices."
        warning(error_message)
        raise ValueError(error_message)
        
    ras_class_names = []
    for file in ras_file_names:
        ras_class_names.append(file.rsplit(".", 1)[0])
    ras_list = []
    class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
    for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
        ras = read_dataset(ras_matrix, "ras dataset")
        ras.replace("None", None, inplace=True)
        ras.set_index("Reactions", drop=True, inplace=True)
        ras = ras.T
        ras = ras.astype(float)
        if(len(ras_file_list)>1):
            # Append class name to patient id (DataFrame index)
            ras.index = [f"{idx}_{ras_class_name}" for idx in ras.index]
        else:
            ras.index = [f"{idx}" for idx in ras.index]
        ras_list.append(ras)
        for patient_id in ras.index:
            class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
    
        
    # Concatenate all RAS DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=0)
    # Normalize RAS values column-wise by max RAS
        ras_combined = ras_combined.div(ras_combined.max(axis=0))
        ras_combined.dropna(axis=1, how='all', inplace=True)

    model = modelUtils.build_cobra_model_from_csv(ARGS.model_upload)

    validation = modelUtils.validate_model(model)

    print("\n=== MODEL VALIDATION ===")
    for key, value in validation.items():
        print(f"{key}: {value}")


    generate_bounds_model(model, ras=ras_combined, output_folder=ARGS.output_path,
                    save_models=ARGS.save_models, save_models_path=ARGS.save_models_path,
                    save_models_format=ARGS.save_models_format)
    class_assignments.to_csv(ARGS.cell_class, sep='\t', index=False)


    return
        
##############################################################################
if __name__ == "__main__":
    main()