def write_split_tad(TAD1, TAD2, contact_map_file1, contact_map_file2, map1,
map2, up1, up2, adjust_quality, output, start1, end,
aliases1, aliases2):
TAD_s, TAD1_only, TAD2_only = TAD_matching(TAD2, TAD1)
merged_TADs, D2, split_TAD = corner_split_score(TAD1, TAD2, TAD_s)
if adjust_quality == 0:
split1_1, split2_1, split_TAD_location, merged_TAD_location = split_region(
contact_map_file2, contact_map_file1, split_TAD, merged_TADs, up2,
up1, 1)
elif adjust_quality == 1:
ratio1 = get_ratio(map1, TAD1, up1)
ratio2 = get_ratio(map2, TAD2, up2)
fold = np.mean(ratio1) / np.mean(ratio2)
split1_1, split2_1, split_TAD_location, merged_TAD_location = split_region(
contact_map_file2, contact_map_file1, split_TAD, merged_TADs, up2,
up1, fold)
else:
print(
"\nusage:\npython3 DACTAD.py TAD_calculator <contact_map_file_paths> [optional arguments]\n\n"
"for more help, please try: python DACTAD.py TAD_calculator -h\n")
try:
if split1_1 == 0:
print('No split TAD')
except:
if (not os.path.exists(output)):
print('path does not exist\n')
else:
scc = similarity_score.similarity_scc(contact_map_file1,
contact_map_file2,
merged_TAD_location[:, 0:3],
output)
Laplacian = similarity_score.similarity_Laplacian(
map1, map2, merged_TAD_location[:, 0:3])
hash = similarity_score.hash_similarity(
map1, map2, merged_TAD_location[:, 0:3])
loc_u2 = np.c_[merged_TAD_location, scc[:(-1), 3], Laplacian[:, 3],
hash[:, 3]]
np.savetxt(
os.path.join(
output, aliases1 + '->' + aliases2 + '.' + str(start1) +
'.' + end + '.split.txt'), split_TAD_location)
np.savetxt(
os.path.join(
output, aliases1 + '->' + aliases2 + '.' + str(start1) +
'.' + end + '.merge.txt'), loc_u2)
def TAD_similarity(command='TAD_similarity'):
if (len(sys.argv) < 3) and ('-h' not in sys.argv) and ('--help' not in sys.argv):
# at least two parameter need to be specified, will print help message if no parameter is specified
print("\nusage:\npython3 TADsplimer.py TAD_similarity <contact_map_file_paths> [optional arguments]\n\n"
"for more help, please try: python3 TADsplimer.py TAD_similarity -h\n")
return 0
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
usage="\n\npython3 TADsplimer.py TAD_similarity <contact_map_file_paths> "
"[optional arguments]\n\n", description='')
parser.add_argument('command', default=None,
help="set as 'TAD_similarity' to calculate similarity of two TADs")
parser.add_argument('-c', '--contact_maps', dest="contact_map", default=None,
help="paths to Hi-C contact maps in two conditions. paths must be separated by the comma ','.")
parser.add_argument('-t', '--TAD', dest="TAD", default=None,
help="input files of TADs for two compared Hi-C contact maps. Paths must be separated by the"
" comma ','.")
parser.add_argument('-o', '--output', dest="output", default=None,
help="path to output files")
args = parser.parse_args()
file1 = args.contact_map.split(',')
file2 = args.TAD.split(',')
map1 = np.loadtxt(file1[0])
map2 = np.loadtxt(file1[1])
TAD1 = np.loadtxt(file2[0])
TAD2 = np.loadtxt(file2[1])
head1, tail1 = os.path.split(file1[0])
base1 = os.path.splitext(tail1)
head2, tail2 = os.path.split(file1[1])
base2 = os.path.splitext(tail2)
TAD = np.concatenate((TAD1, TAD2), axis=0)
scc = similarity_score.similarity_scc(map1, map2, TAD)
Laplacian = similarity_score.similarity_Laplacian(map1, map2, TAD)
hash = similarity_score.hash_similarity(map1, map2, TAD)
np.savetxt(os.path.join(args.output, base1[0]+'_'+base2[0]+'_scc.txt'), scc, delimiter='\t')
np.savetxt(os.path.join(args.output, base1[0]+'_'+base2[0]+'_laplacian.txt'), Laplacian, delimiter='\t')
np.savetxt(os.path.join(args.output, base1[0]+'_'+base2[0]+'_hash.txt'), hash, delimiter='\t')
def split_TADs_alternate(command='split_TADs_alternate'):
'''
corner split algorithm for identifying split TAD
'''
if (len(sys.argv) < 3) and ('-h' not in sys.argv) and ('--help'
not in sys.argv):
# at least two parameter need to be specified, will print help message if no parameter is specified
print(
"\nusage:\npython3 TADsplimer.py TAD_calculator <contact_map_file_paths> [optional arguments]\n\n"
"for more help, please try: python3 TADsplimer.py TAD_calculator -h\n"
)
return 0
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
usage="\n\npython3 TADsplimer.py corner_split <contact_map_file_paths> "
"[optional arguments]\n\n",
description='')
parser.add_argument(
'command',
default=None,
help="set as 'split_TADs_alternate' to identify split TAD")
parser.add_argument(
'-c',
'--contact_maps',
dest="contact_map",
default=None,
help=
"paths to Hi-C contact maps in two conditions. paths must be separated by the comma ','."
)
parser.add_argument(
'--contact_maps_aliases',
dest="aliases",
default=None,
help=
"A set of short aliases for the contact map. Paths must be separated by the comma ','."
)
parser.add_argument(
'-t',
'--TAD',
dest="TAD",
default=None,
help=
"input files of TADs for two compared Hi-C contact maps. Paths must be separated by the"
" comma ','.")
parser.add_argument(
'-u',
'--up_cutoff',
dest="up",
default="0,0",
help=
"up cutoff for two compared Hi-C contact maps, paths must be separated by the comma ','."
)
parser.add_argument(
'-j',
'--adjust_quality',
dest="adjust_quality",
default=0,
type=int,
help=
"set as 1 to normalize sequence quality for two Hi-C contact maps, set as 0 not to "
"normalize sequence quality for two Hi-C contact maps")
parser.add_argument('-o',
'--output',
dest="output",
default=None,
help="path to output files")
parser.add_argument(
'-d',
'--split_direction',
dest="direction",
default=0,
type=int,
help=
"set as 0: output TADs split in both two contact maps, set as 1: output TADs split in "
"contact map 1, set as 2: output TADs split in contact map 2")
args = parser.parse_args()
file = args.contact_map.split(',')
TAD = args.TAD.split(',')
up = args.up.split(',')
aliases = args.aliases.split(',')
TAD1 = np.loadtxt(TAD[0])
TAD2 = np.loadtxt(TAD[1])
TAD_s, TAD1_only, TAD2_only = TAD_split.TAD_matching(TAD2, TAD1)
D1, D2, D3 = TAD_split.corner_split_score(TAD1, TAD2, TAD_s)
map1 = np.loadtxt(file[0])
map2 = np.loadtxt(file[1])
up1, _, _ = TAD_calling.file_split(TAD[0], outfile="", print_subcontact=1)
up2, _, _ = TAD_calling.file_split(TAD[1], outfile="", print_subcontact=1)
if up[0] != 0 or up[1] != 0:
up1 = float(up[0])
up2 = float(up[1])
if args.adjust_quality == 0:
split1_1, split2_1, loc_d, loc_u = TAD_split.split_region(
file[1], file[0], D3, D1, up2, up1, 1)
elif args.adjust_quality == 1:
map1 = np.loadtxt(file[0])
map2 = np.loadtxt(file[1])
ratio1 = TAD_split.get_ratio(map1, TAD1, float(up[0]))
ratio2 = TAD_split.get_ratio(map2, TAD2, float(up[1]))
fold = np.mean(ratio1) / np.mean(ratio2)
split1_1, split2_1, loc_d, loc_u = TAD_split.split_region(
file[1], file[0], D3, D1, up2, up1, fold)
else:
print(
"\nusage:\npython3 TADsplimer.py TAD_calculator <contact_map_file_paths> [optional arguments]\n\n"
"for more help, please try: python3 TADsplimer.py TAD_calculator -h\n"
)
try:
if split1_1 == 0:
print('No split TAD')
except:
if (not os.path.exists(args.output)):
os.makedirs(args.output)
else:
scc = similarity_score.similarity_scc(map1, map2, loc_u[:, 0:3])
Laplacian = similarity_score.similarity_Laplacian(
map1, map2, loc_u[:, 0:3])
hash = similarity_score.hash_similarity(map1, map2, loc_u[:, 0:3])
loc_u2 = np.c_[loc_u, scc[:, 3], Laplacian[:, 3], hash[:, 3]]
np.savetxt(
os.path.join(args.output,
aliases[0] + '->' + aliases[1] + '.split.txt'),
loc_d)
np.savetxt(
os.path.join(args.output,
aliases[0] + '->' + aliases[1] + '.merge.txt'),
loc_u2)