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 = "")
if algorithm == "REC":
crossed_output = functions.run_crossed(
filename = crossed_test,
model = os.path.join(model,
data['rec_crossed_model']),
output = "test_crossed_results_" + args.output,
base_folder = base_folder, folder="")
#Check if output folder exists and remove
if os.path.exists(unique):
shutil.rmtree(unique)
os.mkdir(unique)
#Convert input files to RIC format
ric_positives = functions.convert_ric(
filename=args.positive, length=length,
output="train_ric_positives.fasta", folder=unique)
ric_negatives = functions.convert_ric(
filename=args.negative, length=length,
output="train_ric_negatives.fasta", folder=unique)
#Aply RIC to the test files
ric_positives_output = functions.run_ric(test_file=ric_positives,
length=length, base_folder=base_folder,
training_file=ric_positives, output="test_ric_positives",
folder=unique)
ric_negatives_output = functions.run_ric(test_file=ric_negatives,
length=length, base_folder=base_folder,
training_file=ric_positives, output="test_ric_negatives",
folder=unique)
os.remove(ric_negatives)
#Read scores
ric_positive_scores = functions.read_ric(
filename=ric_positives_output)
os.remove(ric_positives_output)
ric_negative_scores = functions.read_ric(
filename=ric_negatives_output)
os.remove(ric_negatives_output)
ric_value_fdr = functions.get_threshold_unique(
positive = ric_positive_scores,
