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1 import math
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2 import argparse
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3
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4 import numpy as np
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5 import pickle as pk
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6 import pandas as pd
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7
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8 from typing import Optional, List, Dict
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9
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10 import utils.general_utils as utils
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11 import utils.reaction_parsing as reactionUtils
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12
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13 ########################## argparse ##########################################
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14 ARGS :argparse.Namespace
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15 def process_args(args:List[str] = None) -> argparse.Namespace:
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16 """
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17 Processes command-line arguments.
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18
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19 Args:
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20 args (list): List of command-line arguments.
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21
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22 Returns:
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23 Namespace: An object containing parsed arguments.
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24 """
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25 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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26 description = 'process some value\'s'+
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27 ' abundances and reactions to create RPS scores.')
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28
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29 parser.add_argument("-rl", "--model_upload", type = str,
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30 help = "path to input file containing the reactions")
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31
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32 # model_upload custom
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33 parser.add_argument('-td', '--tool_dir',
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34 type = str,
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35 required = True,
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36 help = 'your tool directory')
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37 parser.add_argument('-ol', '--out_log',
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38 help = "Output log")
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39 parser.add_argument('-id', '--input',
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40 type = str,
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41 required = True,
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42 help = 'input dataset')
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43 parser.add_argument('-rp', '--rps_output',
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44 type = str,
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45 required = True,
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46 help = 'rps output')
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47
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48 args = parser.parse_args(args)
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49 return args
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50
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51 ############################ dataset name #####################################
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52 def name_dataset(name_data :str, count :int) -> str:
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53 """
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54 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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55
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56 Args:
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57 name_data : name associated with the dataset (from frontend input params)
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58 count : counter from 1 to make these names unique (external)
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59
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60 Returns:
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61 str : the name made unique
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62 """
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63 if str(name_data) == 'Dataset':
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64 return str(name_data) + '_' + str(count)
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65 else:
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66 return str(name_data)
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67
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68 ############################ get_abund_data ####################################
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69 def get_abund_data(dataset: pd.DataFrame, cell_line_index:int) -> Optional[pd.Series]:
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70 """
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71 Extracts abundance data and turns it into a series for a specific cell line from the dataset, which rows are
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72 metabolites and columns are cell lines.
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73
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74 Args:
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75 dataset (pandas.DataFrame): The DataFrame containing abundance data for all cell lines and metabolites.
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76 cell_line_index (int): The index of the cell line of interest in the dataset.
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77
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78 Returns:
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79 pd.Series or None: A series containing abundance values for the specified cell line.
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80 The name of the series is the name of the cell line.
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81 Returns None if the cell index is invalid.
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82 """
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83 if cell_line_index < 0 or cell_line_index >= len(dataset.index):
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84 print(f"Errore: This cell line index: '{cell_line_index}' is not valid.")
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85 return None
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86
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87 cell_line_name = dataset.columns[cell_line_index]
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88 abundances_series = dataset[cell_line_name][1:]
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89
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90 return abundances_series
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91
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92 ############################ clean_metabolite_name ####################################
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93 def clean_metabolite_name(name :str) -> str:
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94 """
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95 Removes some characters from a metabolite's name, provided as input, and makes it lowercase in order to simplify
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96 the search of a match in the dictionary of synonyms.
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97
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98 Args:
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99 name : the metabolite's name, as given in the dataset.
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100
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101 Returns:
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102 str : a new string with the cleaned name.
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103 """
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104 return "".join(ch for ch in name if ch not in ",;-_'([{ }])").lower()
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105
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106 ############################ get_metabolite_id ####################################
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107 def get_metabolite_id(name :str, syn_dict :Dict[str, List[str]]) -> str:
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108 """
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109 Looks through a dictionary of synonyms to find a match for a given metabolite's name.
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110
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111 Args:
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112 name : the metabolite's name, as given in the dataset.
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113 syn_dict : the dictionary of synonyms, using unique identifiers as keys and lists of clean synonyms as values.
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114
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115 Returns:
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116 str : the internal :str unique identifier of that metabolite, used in all other parts of the model in use.
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117 An empty string is returned if a match isn't found.
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118 """
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119 name = clean_metabolite_name(name)
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120 for id, synonyms in syn_dict.items():
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121 if name in synonyms:
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122 return id
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123
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124 return ""
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125
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126 ############################ check_missing_metab ####################################
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127 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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128 """
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129 Check for missing metabolites in the abundances dictionary compared to the reactions dictionary and update abundances accordingly.
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130
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131 Parameters:
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132 reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and
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133 stoichiometric coefficients as values.
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134 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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135 cell_lines_amt : amount of cell lines, needed to add a new list of abundances for missing metabolites.
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136
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137 Returns:
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138 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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139
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140 Side effects:
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141 dataset_by_rows : mut
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142 """
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143 missing_list = []
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144 for reaction in reactions.values():
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145 for metabolite in reaction.keys():
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146 if metabolite not in dataset_by_rows:
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147 dataset_by_rows[metabolite] = [1] * cell_lines_amt
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148 missing_list.append(metabolite)
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149
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150 return missing_list
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151
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152 ############################ calculate_rps ####################################
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153 def calculate_rps(reactions: Dict[str, Dict[str, int]], abundances: Dict[str, float], black_list: List[str], missing_list: List[str], substrateFreqTable: Dict[str, int]) -> Dict[str, float]:
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154 """
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155 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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156 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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157 for each reaction using the provided coefficient and abundance values. The value is then normalized, based on how frequent the metabolite is in the selected model's reactions,
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158 and log-transformed.
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159
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160 Parameters:
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161 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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162 abundances (dict): A dictionary representing metabolite abundances where keys are metabolite names and values are their corresponding abundances.
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163 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
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164 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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165 substrateFreqTable (dict): A dictionary where each metabolite name (key) is associated with how many times it shows up in the model's reactions (value).
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166
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167 Returns:
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168 dict: A dictionary containing Reaction Propensity Scores (RPS) where keys are reaction names and values are the corresponding RPS scores.
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169 """
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170 rps_scores = {}
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171
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172 for reaction_name, substrates in reactions.items():
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173 total_contribution = 0
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174 metab_significant = False
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175 for metabolite, stoichiometry in substrates.items():
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176 abundance = 1 if math.isnan(abundances[metabolite]) else abundances[metabolite]
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177 if metabolite not in black_list and metabolite not in missing_list:
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178 metab_significant = True
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179
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180 total_contribution += math.log((abundance + np.finfo(float).eps) / substrateFreqTable[metabolite]) * stoichiometry
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181
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182 rps_scores[reaction_name] = total_contribution if metab_significant else math.nan
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183
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184 return rps_scores
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185
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186 ############################ rps_for_cell_lines ####################################
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187 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]], substrateFreqTable: Dict[str, int]) -> None:
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188 """
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189 Calculate Reaction Propensity Scores (RPS) for each cell line represented in the dataframe and creates an output file.
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190
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191 Parameters:
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192 dataset : the dataset's data, by rows
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193 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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194 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
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195 syn_dict (dict): A dictionary where keys are general metabolite names and values are lists of possible synonyms.
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196 substrateFreqTable (dict): A dictionary where each metabolite name (key) is associated with how many times it shows up in the model's reactions (value).
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197
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198 Returns:
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199 None
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200 """
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201
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202 cell_lines = dataset[0][1:]
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203 abundances_dict = {}
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204
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205 for row in dataset[1:]:
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206 id = get_metabolite_id(row[0], syn_dict) #if translationIsApplied else row[0]
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207 if id:
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208 abundances_dict[id] = list(map(utils.Float(), row[1:]))
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209
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210 missing_list = check_missing_metab(reactions, abundances_dict, len((cell_lines)))
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211
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212 rps_scores :Dict[Dict[str, float]] = {}
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213 for pos, cell_line_name in enumerate(cell_lines):
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214 abundances = { metab : abundances[pos] for metab, abundances in abundances_dict.items() }
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215
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216 rps_scores[cell_line_name] = calculate_rps(reactions, abundances, black_list, missing_list, substrateFreqTable)
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217
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218 df = pd.DataFrame.from_dict(rps_scores)
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219 df = df.loc[list(reactions.keys()),:]
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220 print(df.head(10))
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221 df.index.name = 'Reactions'
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222 df.to_csv(ARGS.rps_output, sep='\t', na_rep='None', index=True)
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223
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224 ############################ main ####################################
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225 def main(args:List[str] = None) -> 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(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 tmp_dict = None
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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 # substrateFreqTable = pk.load(open(ARGS.tool_dir + '/local/pickle files/substrate_frequencies.pickle', 'rb'))
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247
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248 #elif ARGS.reaction_choice == 'custom':
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249 reactions = reactionUtils.parse_custom_reactions(ARGS.model_upload)
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250 for r, s in reactions.items():
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251 tmp_list = list(s.keys())
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252 for k in tmp_list:
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253 if k[-2] == '_':
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254 s[k[:-2]] = s.pop(k)
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255 substrateFreqTable = {}
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256 for _, substrates in reactions.items():
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257 for substrateName, _ in substrates.items():
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258 if substrateName not in substrateFreqTable: substrateFreqTable[substrateName] = 0
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259 substrateFreqTable[substrateName] += 1
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260
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261 print(f"Reactions: {reactions}")
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262 print(f"Substrate Frequencies: {substrateFreqTable}")
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263 print(f"Synonyms: {syn_dict}")
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264 tmp_dict = {}
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265 for metabName, freq in substrateFreqTable.items():
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266 tmp_metabName = clean_metabolite_name(metabName)
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267 for syn_key, syn_list in syn_dict.items():
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268 if tmp_metabName in syn_list or tmp_metabName == clean_metabolite_name(syn_key):
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269 print(f"Mapping {tmp_metabName} to {syn_key}")
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270 tmp_dict[syn_key] = syn_list
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271 tmp_dict[syn_key].append(tmp_metabName)
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272
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273 rps_for_cell_lines(dataset, reactions, black_list, syn_dict, substrateFreqTable)
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274 print('Execution succeded')
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275
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276 ##############################################################################
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277 if __name__ == "__main__": main()
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