Mercurial > repos > bimib > cobraxy
comparison COBRAxy/ras_to_bounds.py @ 94:e844f7dab6fe draft
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| author | luca_milaz |
|---|---|
| date | Sun, 13 Oct 2024 11:43:08 +0000 |
| parents | 7e703e546998 |
| children | 54ded7f28a60 |
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| 93:7e703e546998 | 94:e844f7dab6fe |
|---|---|
| 52 parser.add_argument('-ir', '--input_ras', | 52 parser.add_argument('-ir', '--input_ras', |
| 53 type=str, | 53 type=str, |
| 54 required = False, | 54 required = False, |
| 55 help = 'input ras') | 55 help = 'input ras') |
| 56 | 56 |
| 57 parser.add_argument('-rn', '--names', | |
| 58 type=str, | |
| 59 help = 'ras class names') | |
| 60 | |
| 57 parser.add_argument('-rs', '--ras_selector', | 61 parser.add_argument('-rs', '--ras_selector', |
| 58 required = True, | 62 required = True, |
| 59 type=utils.Bool("using_RAS"), | 63 type=utils.Bool("using_RAS"), |
| 60 help = 'ras selector') | 64 help = 'ras selector') |
| 61 | |
| 62 parser.add_argument('-c', '--classes', | |
| 63 type = str, | |
| 64 required = False, | |
| 65 help = 'input classes') | |
| 66 | 65 |
| 67 parser.add_argument('-cc', '--cell_class', | 66 parser.add_argument('-cc', '--cell_class', |
| 68 type = str, | 67 type = str, |
| 69 help = 'output of cell class') | 68 help = 'output of cell class') |
| 69 | |
| 70 | 70 |
| 71 ARGS = parser.parse_args() | 71 ARGS = parser.parse_args() |
| 72 return ARGS | 72 return ARGS |
| 73 | 73 |
| 74 ########################### warning ########################################### | 74 ########################### warning ########################################### |
| 126 for reaction in rxns_ids: | 126 for reaction in rxns_ids: |
| 127 if reaction in ras_row.index: | 127 if reaction in ras_row.index: |
| 128 scaling_factor = ras_row[reaction] | 128 scaling_factor = ras_row[reaction] |
| 129 lower_bound=model.reactions.get_by_id(reaction).lower_bound | 129 lower_bound=model.reactions.get_by_id(reaction).lower_bound |
| 130 upper_bound=model.reactions.get_by_id(reaction).upper_bound | 130 upper_bound=model.reactions.get_by_id(reaction).upper_bound |
| 131 #warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor)) | |
| 131 valMax=float((upper_bound)*scaling_factor) | 132 valMax=float((upper_bound)*scaling_factor) |
| 132 valMin=float((lower_bound)*scaling_factor) | 133 valMin=float((lower_bound)*scaling_factor) |
| 133 if upper_bound!=0 and lower_bound==0: | 134 if upper_bound!=0 and lower_bound==0: |
| 134 model.reactions.get_by_id(reaction).upper_bound=valMax | 135 model.reactions.get_by_id(reaction).upper_bound=valMax |
| 135 if upper_bound==0 and lower_bound!=0: | 136 if upper_bound==0 and lower_bound!=0: |
| 188 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"]) | 189 rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"]) |
| 189 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"]) | 190 rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"]) |
| 190 | 191 |
| 191 if ras is not None: | 192 if ras is not None: |
| 192 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows()) | 193 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows()) |
| 194 #for cellName, ras_row in ras.iterrows(): | |
| 195 #process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder) | |
| 193 else: | 196 else: |
| 194 model_new = model.copy() | 197 model_new = model.copy() |
| 195 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids) | 198 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids) |
| 196 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"]) | 199 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"]) |
| 197 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True) | 200 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True) |
| 216 | 219 |
| 217 ARGS.output_folder = 'ras_to_bounds/' | 220 ARGS.output_folder = 'ras_to_bounds/' |
| 218 | 221 |
| 219 if(ARGS.ras_selector == True): | 222 if(ARGS.ras_selector == True): |
| 220 ras_file_list = ARGS.input_ras.split(",") | 223 ras_file_list = ARGS.input_ras.split(",") |
| 221 if(len(ras_file_list)>1): | 224 ras_file_names = ARGS.names.split(",") |
| 222 ras_class_names = [cls.strip() for cls in ARGS.classes.split(',')] | 225 ras_class_names = [] |
| 223 else: | 226 for file in ras_file_names: |
| 224 ras_class_names = ["placeHolder"] | 227 ras_class_names.append(file.split(".")[0]) |
| 225 ras_list = [] | 228 ras_list = [] |
| 226 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"]) | 229 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"]) |
| 227 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names): | 230 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names): |
| 228 ras = read_dataset(ras_matrix, "ras dataset") | 231 ras = read_dataset(ras_matrix, "ras dataset") |
| 229 ras.replace("None", None, inplace=True) | 232 ras.replace("None", None, inplace=True) |
| 230 ras.set_index("Reactions", drop=True, inplace=True) | 233 ras.set_index("Reactions", drop=True, inplace=True) |
| 231 ras = ras.T | 234 ras = ras.T |
| 232 ras = ras.astype(float) | 235 ras = ras.astype(float) |
| 233 ras_list.append(ras) | 236 ras_list.append(ras) |
| 234 for patient_id in ras.index: | 237 for patient_id in ras.index: |
| 235 class_assignments = class_assignments.append({"Patient_ID": patient_id, "Class": ras_class_name}, ignore_index=True) | 238 class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name] |
| 239 | |
| 236 | 240 |
| 237 # Concatenate all ras DataFrames into a single DataFrame | 241 # Concatenate all ras DataFrames into a single DataFrame |
| 238 ras_combined = pd.concat(ras_list, axis=1) | 242 ras_combined = pd.concat(ras_list, axis=0) |
| 239 # Normalize the RAS values by max RAS | 243 # Normalize the RAS values by max RAS |
| 240 ras_combined = ras_combined.div(ras_combined.max(axis=0)) | 244 ras_combined = ras_combined.div(ras_combined.max(axis=0)) |
| 241 ras_combined = ras_combined.fillna(0) | 245 ras_combined = ras_combined.fillna(0) |
| 242 | 246 |
| 243 | 247 |
| 259 medium = df_mediums[[ARGS.medium_selector]] | 263 medium = df_mediums[[ARGS.medium_selector]] |
| 260 medium = medium[ARGS.medium_selector].to_dict() | 264 medium = medium[ARGS.medium_selector].to_dict() |
| 261 | 265 |
| 262 if(ARGS.ras_selector == True): | 266 if(ARGS.ras_selector == True): |
| 263 generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_folder) | 267 generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_folder) |
| 264 if(len(ras_list)>1): | 268 class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False) |
| 265 class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False) | |
| 266 else: | 269 else: |
| 267 generate_bounds(model, medium, output_folder=ARGS.output_folder) | 270 generate_bounds(model, medium, output_folder=ARGS.output_folder) |
| 268 | 271 |
| 269 pass | 272 pass |
| 270 | 273 |