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1 import re
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2 import sys
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3 import csv
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4 import math
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5 import argparse
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6
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7 import numpy as np
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8 import pickle as pk
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9 import pandas as pd
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10
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11 from enum import Enum
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12 from typing import Optional, List, Dict, Tuple
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13
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14 import utils.general_utils as utils
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15 import utils.reaction_parsing as reactionUtils
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16
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17 ########################## argparse ##########################################
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18 ARGS :argparse.Namespace
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19 def process_args() -> argparse.Namespace:
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20 """
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21 Processes command-line arguments.
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22
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23 Args:
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24 args (list): List of command-line arguments.
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25
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26 Returns:
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27 Namespace: An object containing parsed arguments.
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28 """
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29 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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30 description = 'process some value\'s'+
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31 ' abundances and reactions to create RPS scores.')
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32 parser.add_argument('-rc', '--reaction_choice',
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33 type = str,
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34 default = 'default',
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35 choices = ['default','custom'],
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36 help = 'chose which type of reaction dataset you want use')
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37 parser.add_argument('-cm', '--custom',
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38 type = str,
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39 help='your dataset if you want custom reactions')
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40 parser.add_argument('-td', '--tool_dir',
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41 type = str,
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42 required = True,
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43 help = 'your tool directory')
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44 parser.add_argument('-ol', '--out_log',
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45 help = "Output log")
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46 parser.add_argument('-id', '--input',
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47 type = str,
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48 help = 'input dataset')
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49 parser.add_argument('-rp', '--rps_output',
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50 type = str,
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51 required = True,
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52 help = 'rps output')
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53
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54 args = parser.parse_args()
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55 return args
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56
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57 ############################ dataset name #####################################
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58 def name_dataset(name_data :str, count :int) -> str:
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59 """
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60 Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique.
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61
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62 Args:
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63 name_data : name associated with the dataset (from frontend input params)
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64 count : counter from 1 to make these names unique (external)
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65
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66 Returns:
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67 str : the name made unique
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68 """
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69 if str(name_data) == 'Dataset':
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70 return str(name_data) + '_' + str(count)
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71 else:
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72 return str(name_data)
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73
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74
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75 ############################ get_abund_data ####################################
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76 def get_abund_data(dataset: pd.DataFrame, cell_line_index:int) -> Optional[pd.Series]:
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77 """
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78 Extracts abundance data and turns it into a series for a specific cell line from the dataset, which rows are
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79 metabolites and columns are cell lines.
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80
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81 Args:
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82 dataset (pandas.DataFrame): The DataFrame containing abundance data for all cell lines and metabolites.
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83 cell_line_index (int): The index of the cell line of interest in the dataset.
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84
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85 Returns:
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86 pd.Series or None: A series containing abundance values for the specified cell line.
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87 The name of the series is the name of the cell line.
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88 Returns None if the cell index is invalid.
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89 """
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90 if cell_line_index < 0 or cell_line_index >= len(dataset.index):
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91 print(f"Errore: This cell line index: '{cell_line_index}' is not valid.")
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92 return None
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93
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94 cell_line_name = dataset.columns[cell_line_index]
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95 abundances_series = dataset[cell_line_name][1:]
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96
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97 return abundances_series
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98
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99
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100 ############################ clean_metabolite_name ####################################
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101 def clean_metabolite_name(name :str) -> str:
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102 """
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103 Removes some characters from a metabolite's name, provided as input, and makes it lowercase in order to simplify
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104 the search of a match in the dictionary of synonyms.
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105
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106 Args:
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107 name : the metabolite's name, as given in the dataset.
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108
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109 Returns:
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110 str : a new string with the cleaned name.
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111 """
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112 return "".join(ch for ch in name if ch not in ",;-_'([{ }])").lower()
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113
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114
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115 ############################ get_metabolite_id ####################################
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116 def get_metabolite_id(name :str, syn_dict :Dict[str, List[str]]) -> str:
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117 """
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118 Looks through a dictionary of synonyms to find a match for a given metabolite's name.
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119
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120 Args:
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121 name : the metabolite's name, as given in the dataset.
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122 syn_dict : the dictionary of synonyms, using unique identifiers as keys and lists of clean synonyms as values.
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123
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124 Returns:
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125 str : the internal :str unique identifier of that metabolite, used in all other parts of the model in use.
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126 An empty string is returned if a match isn't found.
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127 """
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128 name = clean_metabolite_name(name)
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129 for id, synonyms in syn_dict.items():
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130 if name in synonyms: return id
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131
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132 return ""
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133
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134 ############################ check_missing_metab ####################################
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135 def check_missing_metab(reactions: Dict[str, Dict[str, int]], dataset_by_rows: Dict[str, List[float]], cell_lines_amt :int) -> List[str]:
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136 """
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137 Check for missing metabolites in the abundances dictionary compared to the reactions dictionary and update abundances accordingly.
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138
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139 Parameters:
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140 reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
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141 dataset_by_rows (dict): A dictionary representing abundances where keys are metabolite names and values are their corresponding abundances for all cell lines.
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142 cell_lines_amt : amount of cell lines, needed to add a new list of abundances for missing metabolites.
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143
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144 Returns:
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145 list[str] : list of metabolite names that were missing in the original abundances dictionary and thus their aboundances were set to 1.
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146
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147 Side effects:
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148 dataset_by_rows : mut
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149 """
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150 missing_list = []
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151 for reaction in reactions.values():
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152 for metabolite in reaction.keys():
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153 if metabolite not in dataset_by_rows:
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154 dataset_by_rows[metabolite] = [1] * cell_lines_amt
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155 missing_list.append(metabolite)
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156
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157 return missing_list
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158
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159 ############################ calculate_rps ####################################
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160 def calculate_rps(reactions: Dict[str, Dict[str, int]], abundances: Dict[str, float], black_list: List[str], missing_list: List[str]) -> Dict[str, float]:
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161 """
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162 Calculate the Reaction Propensity scores (RPS) based on the availability of reaction substrates, for (ideally) each input model reaction and for each sample.
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163 The score is computed as the product of the concentrations of the reacting substances, with each concentration raised to a power equal to its stoichiometric coefficient
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164 for each reaction using the provided coefficient and abundance values.
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165
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166 Parameters:
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167 reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
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168 abundances (dict): A dictionary representing metabolite abundances where keys are metabolite names and values are their corresponding abundances.
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169 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
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170 missing_list (list): A list containing metabolite names that were missing in the original abundances dictionary and thus their values were set to 1.
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171
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172 Returns:
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173 dict: A dictionary containing Reaction Propensity Scores (RPS) where keys are reaction names and values are the corresponding RPS scores.
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174 """
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175 rps_scores = {}
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176
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177 for reaction_name, substrates in reactions.items():
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178 total_contribution = 1
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179 metab_significant = False
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180 for metabolite, stoichiometry in substrates.items():
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181 temp = 1 if math.isnan(abundances[metabolite]) else abundances[metabolite]
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182 if metabolite not in black_list and metabolite not in missing_list:
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183 metab_significant = True
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184 total_contribution *= temp ** stoichiometry
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185
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186 rps_scores[reaction_name] = total_contribution if metab_significant else math.nan
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187
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188 return rps_scores
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189
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190
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191 ############################ rps_for_cell_lines ####################################
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192 def rps_for_cell_lines(dataset: List[List[str]], reactions: Dict[str, Dict[str, int]], black_list: List[str], syn_dict: Dict[str, List[str]]) -> None:
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193 """
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194 Calculate Reaction Propensity Scores (RPS) for each cell line represented in the dataframe and creates an output file.
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195
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196 Parameters:
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197 dataset : the dataset's data, by rows
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198 reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and stoichiometric coefficients as values.
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199 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
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200 syn_dict (dict): A dictionary where keys are general metabolite names and values are lists of possible synonyms.
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201
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202 Returns:
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203 None
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204 """
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205 cell_lines = dataset[0][1:]
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206 abundances_dict = {}
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207
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208 translationIsApplied = ARGS.reaction_choice == "default"
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209 for row in dataset[1:]:
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210 id = get_metabolite_id(row[0], syn_dict) if translationIsApplied else row[0]
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211 if id: abundances_dict[id] = list(map(utils.Float(), row[1:]))
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212
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213 missing_list = check_missing_metab(reactions, abundances_dict, len((cell_lines)))
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214
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215 rps_scores :Dict[Dict[str, float]] = {}
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216 for pos, cell_line_name in enumerate(cell_lines):
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217 abundances = { metab : abundances[pos] for metab, abundances in abundances_dict.items() }
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218 rps_scores[cell_line_name] = calculate_rps(reactions, abundances, black_list, missing_list)
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219
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220 df = pd.DataFrame.from_dict(rps_scores)
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221 df.rename(columns={'Unnamed: 0': 'Reactions'}, inplace=True)
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222 df.to_csv(ARGS.rps_output, sep = '\t', na_rep = "None", index = False)
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223
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224 ############################ main ####################################
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225 def main() -> None:
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226 """
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227 Initializes everything and sets the program in motion based on the fronted input arguments.
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228
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229 Returns:
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230 None
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231 """
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232 global ARGS
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233 ARGS = process_args()
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234
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235 # TODO:use utils functions vvv
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236 with open(ARGS.tool_dir + '/local/pickle files/black_list.pickle', 'rb') as bl:
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237 black_list = pk.load(bl)
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238
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239 with open(ARGS.tool_dir + '/local/pickle files/synonyms.pickle', 'rb') as sd:
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240 syn_dict = pk.load(sd)
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241
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242 dataset = utils.readCsv(utils.FilePath.fromStrPath(ARGS.input), '\t', skipHeader = False)
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243
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244 if ARGS.reaction_choice == 'default':
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245 reactions = pk.load(open(ARGS.tool_dir + '/local/pickle files/reactions.pickle', 'rb'))
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246
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247 elif ARGS.reaction_choice == 'custom':
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248 reactions = reactionUtils.parse_custom_reactions(ARGS.custom)
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249
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250 rps_for_cell_lines(dataset, reactions, black_list, syn_dict)
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251 print('Execution succeded')
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252
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253 ##############################################################################
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254 if __name__ == "__main__":
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255 main() |
