|
4
|
1 import math
|
|
|
2 import argparse
|
|
|
3
|
|
|
4 import numpy as np
|
|
|
5 import pickle as pk
|
|
|
6 import pandas as pd
|
|
|
7
|
|
293
|
8 from typing import Optional, List, Dict
|
|
4
|
9
|
|
|
10 import utils.general_utils as utils
|
|
|
11 import utils.reaction_parsing as reactionUtils
|
|
|
12
|
|
|
13 ########################## argparse ##########################################
|
|
|
14 ARGS :argparse.Namespace
|
|
147
|
15 def process_args(args:List[str] = None) -> argparse.Namespace:
|
|
4
|
16 """
|
|
|
17 Processes command-line arguments.
|
|
|
18
|
|
|
19 Args:
|
|
|
20 args (list): List of command-line arguments.
|
|
|
21
|
|
|
22 Returns:
|
|
|
23 Namespace: An object containing parsed arguments.
|
|
|
24 """
|
|
|
25 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
|
|
|
26 description = 'process some value\'s'+
|
|
|
27 ' abundances and reactions to create RPS scores.')
|
|
402
|
28
|
|
|
29 parser.add_argument("-rl", "--model_upload", type = str,
|
|
|
30 help = "path to input file containing the reactions")
|
|
|
31
|
|
|
32 # model_upload custom
|
|
4
|
33 parser.add_argument('-td', '--tool_dir',
|
|
|
34 type = str,
|
|
|
35 required = True,
|
|
|
36 help = 'your tool directory')
|
|
|
37 parser.add_argument('-ol', '--out_log',
|
|
|
38 help = "Output log")
|
|
|
39 parser.add_argument('-id', '--input',
|
|
|
40 type = str,
|
|
293
|
41 required = True,
|
|
4
|
42 help = 'input dataset')
|
|
|
43 parser.add_argument('-rp', '--rps_output',
|
|
|
44 type = str,
|
|
|
45 required = True,
|
|
|
46 help = 'rps output')
|
|
|
47
|
|
147
|
48 args = parser.parse_args(args)
|
|
4
|
49 return args
|
|
|
50
|
|
|
51 ############################ dataset name #####################################
|
|
|
52 def name_dataset(name_data :str, count :int) -> str:
|
|
|
53 """
|
|
|
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.
|
|
|
55
|
|
|
56 Args:
|
|
|
57 name_data : name associated with the dataset (from frontend input params)
|
|
|
58 count : counter from 1 to make these names unique (external)
|
|
|
59
|
|
|
60 Returns:
|
|
|
61 str : the name made unique
|
|
|
62 """
|
|
|
63 if str(name_data) == 'Dataset':
|
|
|
64 return str(name_data) + '_' + str(count)
|
|
|
65 else:
|
|
|
66 return str(name_data)
|
|
|
67
|
|
|
68 ############################ get_abund_data ####################################
|
|
|
69 def get_abund_data(dataset: pd.DataFrame, cell_line_index:int) -> Optional[pd.Series]:
|
|
|
70 """
|
|
|
71 Extracts abundance data and turns it into a series for a specific cell line from the dataset, which rows are
|
|
|
72 metabolites and columns are cell lines.
|
|
|
73
|
|
|
74 Args:
|
|
|
75 dataset (pandas.DataFrame): The DataFrame containing abundance data for all cell lines and metabolites.
|
|
|
76 cell_line_index (int): The index of the cell line of interest in the dataset.
|
|
|
77
|
|
|
78 Returns:
|
|
|
79 pd.Series or None: A series containing abundance values for the specified cell line.
|
|
|
80 The name of the series is the name of the cell line.
|
|
|
81 Returns None if the cell index is invalid.
|
|
|
82 """
|
|
|
83 if cell_line_index < 0 or cell_line_index >= len(dataset.index):
|
|
|
84 print(f"Errore: This cell line index: '{cell_line_index}' is not valid.")
|
|
|
85 return None
|
|
|
86
|
|
|
87 cell_line_name = dataset.columns[cell_line_index]
|
|
|
88 abundances_series = dataset[cell_line_name][1:]
|
|
|
89
|
|
|
90 return abundances_series
|
|
|
91
|
|
|
92 ############################ clean_metabolite_name ####################################
|
|
|
93 def clean_metabolite_name(name :str) -> str:
|
|
|
94 """
|
|
|
95 Removes some characters from a metabolite's name, provided as input, and makes it lowercase in order to simplify
|
|
|
96 the search of a match in the dictionary of synonyms.
|
|
|
97
|
|
|
98 Args:
|
|
|
99 name : the metabolite's name, as given in the dataset.
|
|
|
100
|
|
|
101 Returns:
|
|
|
102 str : a new string with the cleaned name.
|
|
|
103 """
|
|
|
104 return "".join(ch for ch in name if ch not in ",;-_'([{ }])").lower()
|
|
|
105
|
|
|
106 ############################ get_metabolite_id ####################################
|
|
|
107 def get_metabolite_id(name :str, syn_dict :Dict[str, List[str]]) -> str:
|
|
|
108 """
|
|
|
109 Looks through a dictionary of synonyms to find a match for a given metabolite's name.
|
|
|
110
|
|
|
111 Args:
|
|
|
112 name : the metabolite's name, as given in the dataset.
|
|
|
113 syn_dict : the dictionary of synonyms, using unique identifiers as keys and lists of clean synonyms as values.
|
|
|
114
|
|
|
115 Returns:
|
|
|
116 str : the internal :str unique identifier of that metabolite, used in all other parts of the model in use.
|
|
|
117 An empty string is returned if a match isn't found.
|
|
|
118 """
|
|
|
119 name = clean_metabolite_name(name)
|
|
|
120 for id, synonyms in syn_dict.items():
|
|
381
|
121 if name in synonyms:
|
|
|
122 return id
|
|
4
|
123
|
|
|
124 return ""
|
|
|
125
|
|
|
126 ############################ check_missing_metab ####################################
|
|
|
127 def check_missing_metab(reactions: Dict[str, Dict[str, int]], dataset_by_rows: Dict[str, List[float]], cell_lines_amt :int) -> List[str]:
|
|
|
128 """
|
|
|
129 Check for missing metabolites in the abundances dictionary compared to the reactions dictionary and update abundances accordingly.
|
|
|
130
|
|
|
131 Parameters:
|
|
381
|
132 reactions (dict): A dictionary representing reactions where keys are reaction names and values are dictionaries containing metabolite names as keys and
|
|
|
133 stoichiometric coefficients as values.
|
|
4
|
134 dataset_by_rows (dict): A dictionary representing abundances where keys are metabolite names and values are their corresponding abundances for all cell lines.
|
|
|
135 cell_lines_amt : amount of cell lines, needed to add a new list of abundances for missing metabolites.
|
|
|
136
|
|
|
137 Returns:
|
|
|
138 list[str] : list of metabolite names that were missing in the original abundances dictionary and thus their aboundances were set to 1.
|
|
|
139
|
|
|
140 Side effects:
|
|
|
141 dataset_by_rows : mut
|
|
|
142 """
|
|
|
143 missing_list = []
|
|
|
144 for reaction in reactions.values():
|
|
|
145 for metabolite in reaction.keys():
|
|
|
146 if metabolite not in dataset_by_rows:
|
|
|
147 dataset_by_rows[metabolite] = [1] * cell_lines_amt
|
|
|
148 missing_list.append(metabolite)
|
|
|
149
|
|
|
150 return missing_list
|
|
|
151
|
|
|
152 ############################ calculate_rps ####################################
|
|
293
|
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]:
|
|
4
|
154 """
|
|
|
155 Calculate the Reaction Propensity scores (RPS) based on the availability of reaction substrates, for (ideally) each input model reaction and for each sample.
|
|
|
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
|
|
293
|
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,
|
|
|
158 and log-transformed.
|
|
4
|
159
|
|
|
160 Parameters:
|
|
|
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.
|
|
|
162 abundances (dict): A dictionary representing metabolite abundances where keys are metabolite names and values are their corresponding abundances.
|
|
|
163 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
|
|
|
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.
|
|
293
|
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).
|
|
|
166
|
|
4
|
167 Returns:
|
|
|
168 dict: A dictionary containing Reaction Propensity Scores (RPS) where keys are reaction names and values are the corresponding RPS scores.
|
|
|
169 """
|
|
|
170 rps_scores = {}
|
|
293
|
171
|
|
4
|
172 for reaction_name, substrates in reactions.items():
|
|
326
|
173 total_contribution = 0
|
|
293
|
174 metab_significant = False
|
|
4
|
175 for metabolite, stoichiometry in substrates.items():
|
|
293
|
176 abundance = 1 if math.isnan(abundances[metabolite]) else abundances[metabolite]
|
|
4
|
177 if metabolite not in black_list and metabolite not in missing_list:
|
|
293
|
178 metab_significant = True
|
|
|
179
|
|
|
180 total_contribution += math.log((abundance + np.finfo(float).eps) / substrateFreqTable[metabolite]) * stoichiometry
|
|
4
|
181
|
|
|
182 rps_scores[reaction_name] = total_contribution if metab_significant else math.nan
|
|
|
183
|
|
|
184 return rps_scores
|
|
|
185
|
|
|
186 ############################ rps_for_cell_lines ####################################
|
|
293
|
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:
|
|
4
|
188 """
|
|
|
189 Calculate Reaction Propensity Scores (RPS) for each cell line represented in the dataframe and creates an output file.
|
|
|
190
|
|
|
191 Parameters:
|
|
|
192 dataset : the dataset's data, by rows
|
|
|
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.
|
|
|
194 black_list (list): A list containing metabolite names that should be excluded from the RPS calculation.
|
|
|
195 syn_dict (dict): A dictionary where keys are general metabolite names and values are lists of possible synonyms.
|
|
293
|
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).
|
|
4
|
197
|
|
|
198 Returns:
|
|
|
199 None
|
|
|
200 """
|
|
381
|
201
|
|
4
|
202 cell_lines = dataset[0][1:]
|
|
|
203 abundances_dict = {}
|
|
|
204
|
|
|
205 for row in dataset[1:]:
|
|
381
|
206 id = get_metabolite_id(row[0], syn_dict) #if translationIsApplied else row[0]
|
|
|
207 if id:
|
|
|
208 abundances_dict[id] = list(map(utils.Float(), row[1:]))
|
|
|
209
|
|
4
|
210 missing_list = check_missing_metab(reactions, abundances_dict, len((cell_lines)))
|
|
381
|
211
|
|
4
|
212 rps_scores :Dict[Dict[str, float]] = {}
|
|
|
213 for pos, cell_line_name in enumerate(cell_lines):
|
|
|
214 abundances = { metab : abundances[pos] for metab, abundances in abundances_dict.items() }
|
|
381
|
215
|
|
293
|
216 rps_scores[cell_line_name] = calculate_rps(reactions, abundances, black_list, missing_list, substrateFreqTable)
|
|
4
|
217
|
|
|
218 df = pd.DataFrame.from_dict(rps_scores)
|
|
381
|
219 df = df.loc[list(reactions.keys()),:]
|
|
|
220 print(df.head(10))
|
|
281
|
221 df.index.name = 'Reactions'
|
|
|
222 df.to_csv(ARGS.rps_output, sep='\t', na_rep='None', index=True)
|
|
|
223
|
|
4
|
224 ############################ main ####################################
|
|
147
|
225 def main(args:List[str] = None) -> None:
|
|
4
|
226 """
|
|
|
227 Initializes everything and sets the program in motion based on the fronted input arguments.
|
|
|
228
|
|
|
229 Returns:
|
|
|
230 None
|
|
|
231 """
|
|
|
232 global ARGS
|
|
147
|
233 ARGS = process_args(args)
|
|
4
|
234
|
|
|
235 # TODO:use utils functions vvv
|
|
|
236 with open(ARGS.tool_dir + '/local/pickle files/black_list.pickle', 'rb') as bl:
|
|
|
237 black_list = pk.load(bl)
|
|
|
238
|
|
|
239 with open(ARGS.tool_dir + '/local/pickle files/synonyms.pickle', 'rb') as sd:
|
|
|
240 syn_dict = pk.load(sd)
|
|
|
241
|
|
|
242 dataset = utils.readCsv(utils.FilePath.fromStrPath(ARGS.input), '\t', skipHeader = False)
|
|
381
|
243 tmp_dict = None
|
|
402
|
244 #if ARGS.reaction_choice == 'default':
|
|
|
245 # reactions = pk.load(open(ARGS.tool_dir + '/local/pickle files/reactions.pickle', 'rb'))
|
|
|
246 # substrateFreqTable = pk.load(open(ARGS.tool_dir + '/local/pickle files/substrate_frequencies.pickle', 'rb'))
|
|
4
|
247
|
|
402
|
248 #elif ARGS.reaction_choice == 'custom':
|
|
|
249 reactions = reactionUtils.parse_custom_reactions(ARGS.model_upload)
|
|
|
250 for r, s in reactions.items():
|
|
|
251 tmp_list = list(s.keys())
|
|
|
252 for k in tmp_list:
|
|
|
253 if k[-2] == '_':
|
|
|
254 s[k[:-2]] = s.pop(k)
|
|
|
255 substrateFreqTable = {}
|
|
|
256 for _, substrates in reactions.items():
|
|
|
257 for substrateName, _ in substrates.items():
|
|
|
258 if substrateName not in substrateFreqTable: substrateFreqTable[substrateName] = 0
|
|
|
259 substrateFreqTable[substrateName] += 1
|
|
293
|
260
|
|
381
|
261 print(f"Reactions: {reactions}")
|
|
|
262 print(f"Substrate Frequencies: {substrateFreqTable}")
|
|
|
263 print(f"Synonyms: {syn_dict}")
|
|
|
264 tmp_dict = {}
|
|
|
265 for metabName, freq in substrateFreqTable.items():
|
|
|
266 tmp_metabName = clean_metabolite_name(metabName)
|
|
|
267 for syn_key, syn_list in syn_dict.items():
|
|
|
268 if tmp_metabName in syn_list or tmp_metabName == clean_metabolite_name(syn_key):
|
|
|
269 print(f"Mapping {tmp_metabName} to {syn_key}")
|
|
|
270 tmp_dict[syn_key] = syn_list
|
|
|
271 tmp_dict[syn_key].append(tmp_metabName)
|
|
|
272
|
|
293
|
273 rps_for_cell_lines(dataset, reactions, black_list, syn_dict, substrateFreqTable)
|
|
4
|
274 print('Execution succeded')
|
|
|
275
|
|
|
276 ##############################################################################
|
|
326
|
277 if __name__ == "__main__": main()
|
