Mercurial > repos > bimib > cobraxy

comparison COBRAxy/ras_to_bounds.py @ 60:547942aa1a93 draft

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Uploaded
author luca_milaz
date Sun, 13 Oct 2024 07:21:04 +0000
parents 42a8ccae86cd
children 455e8945d02a
comparison
equal deleted inserted replaced
59:d4162562c8f5 60:547942aa1a93
56 56
57 parser.add_argument('-rs', '--ras_selector', 57 parser.add_argument('-rs', '--ras_selector',
58 required = True, 58 required = True,
59 type=utils.Bool("using_RAS"), 59 type=utils.Bool("using_RAS"),
60 help = 'ras selector') 60 help = 'ras selector')
61
62 parser.add_argument('-c', '--classes',
63 type = str,
64 required = False,
65 help = 'input classes')
66 61
67 parser.add_argument('-cc', '--cell_class', 62 parser.add_argument('-cc', '--cell_class',
68 type = str, 63 type = str,
69 help = 'output of cell class') 64 help = 'output of cell class')
70 65
216 211
217 ARGS.output_folder = 'ras_to_bounds/' 212 ARGS.output_folder = 'ras_to_bounds/'
218 213
219 if(ARGS.ras_selector == True): 214 if(ARGS.ras_selector == True):
220 ras_file_list = ARGS.input_ras.split(",") 215 ras_file_list = ARGS.input_ras.split(",")
221 if(len(ras_file_list)>1): 216 ras_class_names = []
222 ras_class_names = [cls.strip() for cls in ARGS.classes.split(',')] 217 for file in ras_file_list:
223 else: 218 ras_class_names.append(file.split(".")[0])
224 ras_class_names = ["placeHolder"]
225 ras_list = [] 219 ras_list = []
226 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"]) 220 class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
227 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names): 221 for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
228 ras = read_dataset(ras_matrix, "ras dataset") 222 ras = read_dataset(ras_matrix, "ras dataset")
229 ras.replace("None", None, inplace=True) 223 ras.replace("None", None, inplace=True)
230 ras.set_index("Reactions", drop=True, inplace=True) 224 ras.set_index("Reactions", drop=True, inplace=True)
231 ras = ras.T 225 ras = ras.T
232 ras = ras.astype(float) 226 ras = ras.astype(float)
233 ras_list.append(ras) 227 ras_list.append(ras)
234 for patient_id in ras.index: 228 for patient_id in ras.index:
235 class_assignments = pd.concat([class_assignments, pd.DataFrame({"Patient_ID": ras.index, "Class": ras_class_name})]) 229 class_assignments = pd.concat([class_assignments, pd.DataFrame({"Patient_ID": patient_id, "Class": ras_class_name})])
236 230
237 231
238 # Concatenate all ras DataFrames into a single DataFrame 232 # Concatenate all ras DataFrames into a single DataFrame
239 ras_combined = pd.concat(ras_list, axis=1) 233 ras_combined = pd.concat(ras_list, axis=1)
240 # Normalize the RAS values by max RAS 234 # Normalize the RAS values by max RAS