folder = "", extra = data['extra'])
else:
if algorithm == "REC":
crossed_test = functions.convert_crossed(
filename = args.test, length = length,
output = "test_crossed_" + args.output,
features = data['rec_crossed_features'],
positive = True, folder = "", extra = data['extra'])
ric_test = functions.convert_ric(filename = args.test,
length = length, output = "test_ric_" + args.output,
folder = "")
#Apply algorithms
if algorithm == "CROSSED":
crossed_output = functions.run_crossed(filename=crossed_test,
model=os.path.join(model, data['crossed_model']),
output="test_crossed_results_" + args.output,
base_folder=base_folder, folder="")
else:
if algorithm != "rRIC":
ric_output = functions.run_ric(test_file = ric_test,
length = length, base_folder = base_folder,
training_file = os.path.join(os.path.join(
base_folder, "Data"), "classic_mouse_" + str(
length-16) + ".fasta"), output =
"test_ric_results_" + args.output, folder="")
if algorithm != "RIC":
rric_output = functions.run_ric(test_file = ric_test,
length = length, base_folder = base_folder,
training_file = os.path.join(model,
data['ric_template']), output =
"test_rric_results_" + args.output, folder = "")
output="train_crossed_positives.fasta",
features=features, positive=True, folder=unique,
extra=extra)
crossed_negatives = functions.convert_crossed(
filename=args.negative,
length=length,
output="train_crossed_negatives.fasta",
features=features, positive=False, folder=unique,
extra=extra)
#Train CRoSSeD model
model = functions.train_crossed(positive=crossed_positives,
negative=crossed_negatives, model="CRoSSeD_model",
unique=unique, base_folder=base_folder)
#Aply CRoSSeD to input data
crossed_positives_output = functions.run_crossed(
filename=crossed_positives, model=model,
output="test_crossed_positives", base_folder=base_folder,
folder=unique)
crossed_negatives_output = functions.run_crossed(
filename=crossed_negatives, model=model,
output="test_crossed_negatives", base_folder=base_folder,
folder=unique)
#Read scores
crossed_positive_scores = functions.read_crossed(
filename=crossed_positives_output, original=args.positive,
length=length)
crossed_negative_scores = functions.read_crossed(
filename=crossed_negatives_output, original=args.negative,
length=length)
crossed_value_fdr = functions.get_threshold_unique(
positive = crossed_positive_scores,
negative = crossed_negative_scores,