def main(argv):
#Create variables
expression_data = None
motif = None
ppi = None
output_file = "output_panda.txt"
rm_missing = False
lioness_file = False
# Get input options
try:
opts, args = getopt.getopt(argv, 'he:m:p:o:rq:', ['help', 'expression=', 'motif=', 'ppi=', 'out=', 'rm_missing', 'lioness'])
except getopt.GetoptError:
print(__doc__)
sys.exit()
for opt, arg in opts:
if opt in ('-h', '--help'):
print(__doc__)
sys.exit()
elif opt in ('-e', '--expression'):
expression_data = arg
elif opt in ('-m', '--motif'):
motif = arg
elif opt in ('-p', '--ppi'):
ppi = arg
elif opt in ('-o', '--out'):
output_file = arg
elif opt in ('-r', '--rm_missing'):
rm_missing = arg
elif opt in ('-q', '--lioness'):
lioness_file = arg
#Check if required options are given
print('Input data:')
print('Expression:', expression_data)
print('Motif data:', motif)
print('PPI data:', ppi)
if expression_data is None and motif is None:
print('Missing inputs!')
print(__doc__)
sys.exit()
# Run PANDA
print('Start Panda run ...')
panda_obj = Panda(expression_data, motif, ppi, save_tmp=True, remove_missing=rm_missing, keep_expression_matrix=bool(lioness_file))
#panda_obj = pypanda.Panda(expression_data, motif, None, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(None, motif, ppi, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(None, motif, None, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(expression_data, None, ppi, save_tmp=True, remove_missing=rm_missing)
panda_obj.save_panda_results(output_file)
#panda_obj.top_network_plot(top=70, file='panda_topgenes.png')
#indegree = panda_obj.return_panda_indegree()
#outdegree = panda_obj.return_panda_outdegree()
if lioness_file:
from netZooPy.lioness.lioness import Lioness
lioness_obj = Lioness(panda_obj)
lioness_obj.save_lioness_results(lioness_file)
print('All done!')
def test_lioness():
print('Start lioness test!')
#1. First generate temporary PANDA files as inputs for Lioness
ppi = 'tests/puma/ToyData/ToyPPIData.txt'
motif = 'tests/puma/ToyData/ToyMotifData.txt'
expression_data = 'tests/puma/ToyData/ToyExpressionData.txt'
lioness_file = 'Travis'
rm_missing = False
output_file = 'panda.npy'
gt_file = 'tests/panda/test_panda.txt'
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='legacy',
save_memory=False)
# Set parameters
lioness_obj = Lioness(panda_obj, start=1, end=1)
# lioness_obj.save_lioness_results(lioness_file)
# Read first lioness network
res = np.load('lioness_output/lioness.1.npy')
gt = np.load('tests/lioness/lioness.1.npy')
# Compare to ground truth
assert (np.allclose(gt, res))
#2. Testing Lioness with motif set to None to compute Lioness on coexpression networks
motif = None
# Make sure to keep epxression matrix for next step
panda_obj_2 = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
modeProcess='legacy')
lioness_obj_2 = Lioness(panda_obj_2, start=1, end=1)
# lioness_obj.save_lioness_results(lioness_file)
# Read first lioness network
res = np.load('lioness_output/lioness.1.npy')
gt = np.load('tests/lioness/lionessCoexpression.1.npy')
# Compare to ground truth
assert (np.allclose(gt, res))
#3. Testing Lioness in parallel
# Set parameters
os.remove('lioness_output/lioness.1.npy')
lioness_obj = Lioness(panda_obj, ncores=2, start=1, end=2)
# lioness_obj.save_lioness_results(lioness_file)
# Read first lioness network
res = np.load('lioness_output/lioness.1.npy')
gt = np.load('tests/lioness/lioness.1.npy')
# Compare to ground truth
assert (np.allclose(gt, res))
def main(argv):
"""
Description:
Run LIONESS algorithm from the command line.
Usage:
-h, --help: help
-e, --expression: expression matrix (.npy)
-m, --motif: motif matrix, normalized (.npy)
-p, --ppi: ppi matrix, normalized (.npy)
-g, --comp: use cpu (default) or gpu
-r, --pre: number of digits to calcluate
-c, --ncores: number cores
-n, --npy: PANDA network (.npy)
-o, --out: output folder
-f, --format: output format (txt, npy, or mat)
start: to start from nth sample (optional)
end: to end at nth sample (optional, must with start)
Example:
python3 run_lioness.py -e ../../tests/ToyData/ToyExpressionData.txt -m ../../tests/ToyData/ToyMotifData.txt -p ../../tests/ToyData/ToyPPIData.txt -g cpu -r single -c 2 -o /tmp -f npy 1 2
Reference:
Kuijjer, Marieke Lydia, et al. "Estimating sample-specific regulatory networks." Iscience 14 (2019): 226-240.
"""
#Create variables
expression_data = None
motif = None
ppi = None
comp = None
pre = None
ncores = None
save_dir = None
save_fmt = None
try:
opts, args = getopt.getopt(argv, 'he:m:p:g:r:c:n:o:f:', ['help', 'expression=', 'motif=','ppi=','comp=','pre=','ncores=', 'out=', 'format='])
except getopt.GetoptError as err:
print(str(err)) # will print something like "option -a not recognized"
print(__doc__)
return 2
for opt, arg in opts:
if opt in ('-h', '--help'):
print(__doc__)
return 0
elif opt in ('-e', '--expression'):
expression_data = arg
elif opt in ('-m', '--motif'):
motif = arg
elif opt in ('-p', '--ppi'):
ppi = arg
elif opt in ('-g', '--comp'):
comp = arg
elif opt in ('-r', '--pre'):
pre = arg
elif opt in ('-c', '--ncores'):
ncores = arg
elif opt in ('-n'):
panda_net = arg
elif opt in ('-o', '--out'):
save_dir = arg
elif opt in ('-f', '--format'):
save_fmt = arg
else:
print('Unknown option', opt)
return 1
start, end = 1, None
if len(args) == 2:
start, end = map(int, args)
#Check if required options are given
if expression_data is None or motif is None or ppi is None \
or save_dir is None or save_fmt is None:
print('Missing argument!')
print(__doc__)
return 1
else:
print('Input data:')
print('Expression: ', expression_data)
print('Motif matrix: ', motif)
print('PPI matrix: ', ppi)
print('compute core: ', comp)
print('precision: ', pre)
print('n cores: ', ncores)
print('Output folder:', save_dir)
print('Output format:', save_fmt)
print('Sample range: ', start, '-', end)
# Run panda
print('Start LIONESS run ...')
obj = Panda(expression_data, motif, ppi, keep_expression_matrix=True,save_memory=False)
L = Lioness(obj, computing=comp, precision=pre,ncores=ncores,start=start, end=end, save_dir=save_dir, save_fmt=save_fmt)
print('All done!')
def main(argv):
#Create variables
expression_data = None
motif = None
ppi = None
save_dir = None
save_fmt = None
try:
opts, args = getopt.getopt(
argv, 'he:m:p:n:o:f:',
['help', 'expression=', 'motif=', 'ppi=', 'out=', 'format='])
except getopt.GetoptError as err:
print(str(err)) # will print something like "option -a not recognized"
print(__doc__)
return 2
for opt, arg in opts:
if opt in ('-h', '--help'):
print(__doc__)
return 0
elif opt in ('-e', '--expression'):
expression_data = arg
elif opt in ('-m', '--motif'):
motif = arg
elif opt in ('-p', '--ppi'):
ppi = arg
elif opt in ('-n'):
panda_net = arg
elif opt in ('-o', '--out'):
save_dir = arg
elif opt in ('-f', '--format'):
save_fmt = arg
else:
print('Unknown option', opt)
return 1
start, end = 1, None
if len(args) == 2:
start, end = map(int, args)
#Check if required options are given
if expression_data is None or motif is None or ppi is None \
or save_dir is None or save_fmt is None:
print('Missing argument!')
print(__doc__)
return 1
else:
print('Input data:')
print('Expression: ', expression_data)
print('Motif matrix: ', motif)
print('PPI matrix: ', ppi)
print('Output folder:', save_dir)
print('Output format:', save_fmt)
print('Sample range: ', start, '-', end)
# Run panda
print('Start LIONESS run ...')
obj = Panda(expression_data, motif, ppi, keep_expression_matrix=True)
L = Lioness(obj,
start=start,
end=end,
save_dir=save_dir,
save_fmt=save_fmt)
print('All done!')
def main(argv):
"""
Description:
Run PANDA algorithm from the command line.
Inputs:
-h, --help: help
-e, --expression: expression values
-m, --motif: pair file of motif edges, or Pearson correlation matrix when not provided
-p, --ppi: pair file of PPI edges
-o, --out: output file
-r, --rm_missing
-q, --lioness: output for Lioness single sample networks
Example:
python run_panda.py -e ../../tests/ToyData/ToyExpressionData.txt -m ../../tests/ToyData/ToyMotifData.txt -p ../../tests/ToyData/ToyPPIData.txt -o test_panda.txt -q output_panda.txt
Reference:
Glass, Kimberly, et al. "Passing messages between biological networks to refine predicted interactions." PloS one 8.5 (2013): e64832.
"""
#Create variables
expression_data = None
motif = None
ppi = None
output_file = "output_panda.txt"
rm_missing = False
lioness_file = False
# Get input options
try:
opts, args = getopt.getopt(argv, 'he:m:p:o:rq:', [
'help', 'expression=', 'motif=', 'ppi=', 'out=', 'rm_missing',
'lioness'
])
except getopt.GetoptError:
print(__doc__)
sys.exit()
for opt, arg in opts:
if opt in ('-h', '--help'):
print(__doc__)
sys.exit()
elif opt in ('-e', '--expression'):
expression_data = arg
elif opt in ('-m', '--motif'):
motif = arg
elif opt in ('-p', '--ppi'):
ppi = arg
elif opt in ('-o', '--out'):
output_file = arg
elif opt in ('-r', '--rm_missing'):
rm_missing = arg
elif opt in ('-q', '--lioness'):
lioness_file = arg
#Check if required options are given
print('Input data:')
print('Expression:', expression_data)
print('Motif data:', motif)
print('PPI data:', ppi)
if expression_data is None and motif is None:
print('Missing inputs!')
print(__doc__)
sys.exit()
# Run PANDA
print('Start Panda run ...')
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file))
#panda_obj = pypanda.Panda(expression_data, motif, None, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(None, motif, ppi, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(None, motif, None, save_tmp=True, remove_missing=rm_missing)
#panda_obj = pypanda.Panda(expression_data, None, ppi, save_tmp=True, remove_missing=rm_missing)
panda_obj.save_panda_results(output_file)
#panda_obj.top_network_plot(top=70, file='panda_topgenes.png')
#indegree = panda_obj.return_panda_indegree()
#outdegree = panda_obj.return_panda_outdegree()
if lioness_file:
from netZooPy.lioness.lioness import Lioness
lioness_obj = Lioness(panda_obj)
lioness_obj.save_lioness_results(lioness_file)
print('All done!')