def main():
""" Performs NMF decomposition with provided matrix """
args = parse_arguments()
sig.sigProfilerExtractor(input_type = 'matrix', \
output = args.output, \
input_data = args.input_data, \
reference_genome = args.ref, \
opportunity_genome = args.ref, \
context_type = args.context_type, \
exome = args.exome, \
minimum_signatures = args.minimum_signatures, \
maximum_signatures = args.maximum_signatures, \
nmf_replicates = args.nmf_replicates, \
matrix_normalization = args.matrix_normalization, \
nmf_init = args.nmf_init, \
cpu = args.cpu, \
make_decomposition_plots = False, \
resample = args.resample, \
precision = args.precision, \
min_nmf_iterations = args.min_nmf_iterations, \
max_nmf_iterations = args.max_nmf_iterations, \
nmf_test_conv = args.nmf_test_conv, \
nmf_tolerance = args.nmf_tolerance, \
stability = args.stability, \
min_stability = args.min_stability, \
combined_stability = args.combined_stability, \
cosmic_version = args.cosmic_version, \
de_novo_fit_penalty = args.de_novo_fit_penalty, \
nnls_add_penalty = args.nnls_add_penalty, \
nnls_remove_penalty = args.nnls_remove_penalty, \
initial_remove_penalty = args.initial_remove_penalty, \
refit_denovo_signatures = args.refit_denovo_signatures
)
return ()
def test_cpu(data):
sig.sigProfilerExtractor("text",
"example_output",
data,
startProcess=1,
endProcess=2,
totalIterations=3)
def main(argv):
input = ''
output = ''
try:
opts, args = getopt.getopt(argv, "hi:o:", ["ifile=", "ofile="])
except getopt.GetoptError:
print('denovoSig.py -i <inputfile> -o <outputdir>')
sys.exit(2)
if len(sys.argv) <= 1:
print('denovoSig.py -i <inputfile> -o <outputdir>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('denovoSig.py -i <inputfile> -o <outputdir>')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputdir = arg
else:
print('Unknown option', opt)
print('denovoSig.py -i <inputfile> -o <outputdir>')
sys.exit(2)
print('Generating De Novo signatures for input file: ', inputfile)
print('Output directory is: ', outputdir)
#Run De Novo sig extraction
sig.sigProfilerExtractor("table", outputdir, inputfile)
def main():
data = sig.importdata("text")
sig.sigProfilerExtractor("text",
"example_output",
data,
minimum_signatures=1,
maximum_signatures=2,
nmf_replicates=3)
def main():
data = sig.importdata("text")
sig.sigProfilerExtractor("text",
"example_output",
data,
startProcess=1,
endProcess=2,
totalIterations=3)
def test_gpu(data):
if torch.cuda.device_count() > 0:
sig.sigProfilerExtractor("text",
"example_output",
data,
startProcess=1,
endProcess=2,
totalIterations=3,
gpu=True)
def extractSignatures(output,
vcfpath,
genome,
project,
sigClass='SBS',
sigContext='96',
exome=False,
threads=-1):
"""Extracts signatures from a matrix generated by SigProfilerMatrixGenerator"""
sigType = sigClass + sigContext
if exome:
input_data = vcfpath + "/output/" + sigClass + "/" + project + "." + sigType + ".exome"
else:
input_data = vcfpath + "/output/" + sigClass + "/" + project + "." + sigType + ".all"
sig.sigProfilerExtractor("matrix",
output,
input_data,
reference_genome=genome,
opportunity_genome=genome,
context_type=sigType,
minimum_signatures=1,
maximum_signatures=10,
cpu=threads)
from SigProfilerExtractor import decomposition as decomp
signatures = output + "/" + sigType + "/Suggested_Solution/" + sigType + "_De-Novo_Solution/Signatures/" + sigType + "_De-Novo_Signatures.txt"
activities = output + "/" + sigType + "/Suggested_Solution/" + sigType + "_De-Novo_Solution/Activities/" + sigType + "_De-Novo_Activities_refit.txt"
samples = output + "/" + sigType + "/Samples.txt"
#to get all cosmic signatures without filtering
decomp.decompose(signatures,
activities,
samples,
output,
genome_build=genome,
verbose=False,
nnls_add_penalty=0.0,
nnls_remove_penalty=0.0,
initial_remove_penalty=0.0,
de_novo_fit_penalty=0.02)
if os.path.isdir(mk_dir_path) is False:
os.mkdir(mk_dir_path)
sp.call(rf'cp {vcf_file} {mk_dir_path}', shell=True)
# exit(0)
os.chdir(root_dir)
# os.getcwd()
# input_vcf_dir = '/home/jun9485/data/utuc/somatic_call/filtered/snp/'
# path_to_example_table = sig.importdata("vcf")
# path_to_example_table = sig.importdata("vcf")
# print(path_to_example_table)
input_dir_lst = glob(root_dir + vcf_dir_format)
# print(input_dir_lst)
for i in range(len(input_dir_lst)):
input_vcf_dir = input_dir_lst[i]
input_vcf_dir_name = input_vcf_dir.split(r'/')[-1]
out_dir_name = rf'sig_analysis_{input_vcf_dir_name}/'
sig.sigProfilerExtractor('vcf', out_dir_name, input_vcf_dir)
if __name__ == "__main__":
args = parse_args()
if args.directory is None:
current = getcwd()
args.directory = current + "/" + "sigprofiler_sigs"
try:
if not isdir(args.directory):
mkdir(args.directory)
except:
print("ERROR: creation of directory", args.directory,
"failed. Please use the -d option to create a valid directory.")
try:
shutil.copyfile(args.table, args.directory + "/" + args.table)
except:
print("File copy failed.", args.table,
args.directory + "/" + args.table)
sig.sigProfilerExtractor("table",
args.directory,
args.table,
startProcess=args.start,
endProcess=args.end,
totalIterations=args.iters,
cpu=args.cpu,
mtype=args.mtype,
gpu=args.gpu)