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+ + + + + + diff -r 37c343ca4448 -r 69ffff42d658 marea_2_0/model_generator.py --- a/marea_2_0/model_generator.py Fri Jul 05 16:15:30 2024 +0000 +++ b/marea_2_0/model_generator.py Sat Jul 06 09:24:54 2024 +0000 @@ -21,6 +21,16 @@ """ 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, + 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('-ol', '--out_log', help = "Output log") @@ -30,6 +40,11 @@ required = True, help = 'your tool directory') + parser.add_argument('-in', '--model', + required = True, + type=str, + help = 'input model') + parser.add_argument('-im', '--input_medium', required = True, @@ -69,6 +84,11 @@ dataset.to_csv(ARGS.output_folder + name + ".csv", sep = '\t', index = keep_index) +def generate_model(cell_name, ras, medium): + # compute FVA + pass + + ############################# main ########################################### def main() -> None: """ @@ -87,11 +107,16 @@ ARGS.output_folder = 'model_generator/' - - ras_inputs = ARGS.input_ras.split(",") ARGS.output_types = ARGS.output_type.split(",") - ras = pd.read_table(ras_inputs[0], header=0, sep=r'\s+', index_col = 0) + ras = pd.read_table(ARGS.input_ras, header=0, sep=r'\s+', index_col = 0).T + ras.replace("None", None, inplace=True) + + #medium has rows cells and columns medium reactions, not common reactions set to None + medium = pd.read_csv(ARGS.input_medium, sep = '\t', header = 0, engine='python', index_col = 0) + + for index, row in ras.iterrows(): #iterate over cells RAS + generate_model(index, row, medium.loc[index]) pass diff -r 37c343ca4448 -r 69ffff42d658 marea_2_0/model_generator.xml --- a/marea_2_0/model_generator.xml Fri Jul 05 16:15:30 2024 +0000 +++ b/marea_2_0/model_generator.xml Sat Jul 06 09:24:54 2024 +0000 @@ -13,17 +13,29 @@ - + + + + + + + + diff -r 37c343ca4448 -r 69ffff42d658 marea_2_0/utils/CBS_backend.py diff -r 37c343ca4448 -r 69ffff42d658 marea_2_0/utils/flux_analysis.py --- a/marea_2_0/utils/flux_analysis.py Fri Jul 05 16:15:30 2024 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,81 +0,0 @@ -import cobra -import utils -import pandas as pd -from scipy.stats import spearmanr, pearsonr, gaussian_kde -import numpy as np - - -def get_flux_correlation(samples:pd.DataFrame, reference_rxn:str, method:str="spearman", fillNan:bool=True) -> pd.DataFrame: - - df_result = pd.DataFrame(index = samples.columns) - - reference_rxn_data = samples[reference_rxn].values - - coefs = [] - pvals = [] - - for rxn in samples.columns: - if(method == "spearman"): - coef, pval = spearmanr(reference_rxn_data, samples[rxn].values) - elif(method == "pearson"): - coef, pval = pearsonr(reference_rxn_data, samples[rxn].values) - else: - raise utils.ValueErr(method + " is not recognized, only spearman and pearson are supported.") - coefs.append(coef) - pvals.append(pval) - - df_result["p_value"] = pvals - df_result["coefficient"] = coefs - - if(fillNan): - df_result.fillna(0, inplace = True) - - return df_result - -def compute_statistic(samples:pd.DataFrame, method:str="mean")-> pd.DataFrame: - - if(method == "dist"): - columns = [] - for rxn in samples.columns: - columns.append(rxn + "_min") - columns.append(rxn + "_1q") - columns.append(rxn + "_2q") - columns.append(rxn + "_3q") - columns.append(rxn + "_max") - - df_result = pd.DataFrame(columns = columns) - else: - df_result = pd.DataFrame(columns = samples.columns) - - stat_rxns = [] - for rxn in samples.columns: - data = samples[rxn] - if(method == "mean"): - stat_rxns.append(data.mean()) - elif(method == "median"): - stat_rxns.append(data.median()) - elif(method == "mode"): - if(len(set(data)) == 1): - stat_rxns.append(data[0]) - else: - kde = gaussian_kde(data) - x_grid = np.linspace(min(data), max(data), 100) - density = kde(x_grid) - mode_index = np.argmax(density) - stat_rxns.append(x_grid[mode_index]) - elif(method == "dist"): - stat_rxns.append(data.min()) - stat_rxns.append(data.quantile(0.25)) - stat_rxns.append(data.quantile(0.50)) - stat_rxns.append(data.quantile(0.75)) - stat_rxns.append(data.max()) - else: - raise utils.ValueErr(method + " is not recognized, only mean, median, mode and dist are supported.") - - df_result.loc[0] = stat_rxns - - return df_result - - - - \ No newline at end of file diff -r 37c343ca4448 -r 69ffff42d658 marea_2_0/utils/general_utils.py --- a/marea_2_0/utils/general_utils.py Fri Jul 05 16:15:30 2024 +0000 +++ b/marea_2_0/utils/general_utils.py Sat Jul 06 09:24:54 2024 +0000 @@ -4,6 +4,7 @@ import csv import pickle import lxml.etree as ET +import cobra from enum import Enum from itertools import count @@ -547,5 +548,37 @@ path = customPath if self is Model.Custom else FilePath(f"{self.name}_map", FileFormat.SVG, prefix = f"{toolDir}/local/svg metabolic maps/") self.__raiseMissingPathErr(path) return readSvg(path, customErr = DataErr(path, f"custom map in wrong format")) + + def getCOBRAmodel(self, toolDir = ".", customPath :Optional[FilePath] = None, customExtension :Optional[FilePath]=None)->cobra.Model: + if(self is Model.custom): + return self.load_custom_model(customPath, customExtension) + else: + return cobra.io.read_sbml_model(FilePath(f"{self.name}", FileFormat.XML, prefix = f"{toolDir}/local/COBRA models/")) + + def load_custom_model(file_path :FilePath, ext :Optional[FileFormat] = None) -> cobra.Model: + """ + Loads a custom model from a file, either in JSON or XML format. + + Args: + file_path : The path to the file containing the custom model. + ext : explicit file extension. Necessary for standard use in galaxy because of its weird behaviour. + + Raises: + DataErr : if the file is in an invalid format or cannot be opened for whatever reason. + + Returns: + cobra.Model : the model, if successfully opened. + """ + ext = ext if ext else file_path.ext + try: + if ext is FileFormat.XML: + return cobra.io.read_sbml_model(file_path.show()) + + if ext is FileFormat.JSON: + return cobra.io.load_json_model(file_path.show()) + + except Exception as e: raise DataErr(file_path, e.__str__()) + raise DataErr(file_path, + f"Fomat \"{file_path.ext}\" is not recognized, only JSON and XML files are supported.") def __str__(self) -> str: return self.value \ No newline at end of file