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_panda():
#print(os.getcwd())
print('Start Panda run ...')
ppi = 'tests/puma/ToyData/ToyPPIData.txt'
motif = 'tests/puma/ToyData/ToyMotifData.txt'
expression_data = 'tests/puma/ToyData/ToyExpressionData.txt'
lioness_file = ''
rm_missing = False
output_file = 'travis_test_panda.txt'
gt_file = 'tests/panda/union_test_panda.txt'
#1. Union
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='union')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#3. In-degree and out-degree
panda_obj.return_panda_indegree()
panda_obj.return_panda_outdegree()
# Lazy test
assert (round(panda_obj.panda_indegree.iloc[0].loc['force'], 5) == 1.13971)
assert (round(panda_obj.panda_outdegree.iloc[0].loc['force'],
5) == 1030.06840)
#2. Legacy
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess='legacy')
panda_obj.save_panda_results(output_file)
gt_file = 'tests/panda/legacy_test_panda.txt'
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
print('Test panda passed was successful!')
def test_panda():
#print(os.getcwd())
print('Start Panda run ...')
ppi = 'tests/ToyData/ToyPPIData.txt'
motif = 'tests/ToyData/ToyMotifData.txt'
expression_data = 'tests/ToyData/ToyExpressionData.txt'
lioness_file = ''
rm_missing = False
output_file = 'travis_test_panda.txt'
gt_file = 'tests/panda/test_panda.txt'
#1. Vanilla panda
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file))
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
#assert(gt.equals(round(res,3)))
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#2. with argument values
rm_missing = False
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True)
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
assert (np.allclose(gt.iloc[:, 3], res.transpose().values.flatten()))
print('Test panda passed was successful!')
#3. From command line
os.system(
'python3 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'
)
res2 = pd.read_csv(output_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
res2,
check_less_precise=False,
check_exact=False)
def test_panda():
#print(os.getcwd())
print('Start Panda run ...')
ppi = 'tests/puma/ToyData/ToyPPIData.txt'
motif = 'tests/puma/ToyData/ToyMotifData.txt'
expression_data = 'tests/puma/ToyData/ToyExpressionData.txt'
lioness_file = ''
rm_missing = False
output_file = 'travis_test_panda.txt'
gt_file = 'tests/panda/union_test_panda.txt'
gt_file_inter = 'tests/panda/inter_test_panda.txt'
#0. Intersection
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#0.1 Intersection via DataFrame
expression = pd.read_csv(expression_data,
sep='\t',
header=None,
index_col=0)
motif_data = pd.read_csv(motif, sep='\t', names=['source', 'target', 'w'])
ppi_data = pd.read_csv(ppi, sep='\t', header=None)
panda_obj = Panda(expression,
motif_data,
ppi_data,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#0.2 Intersection with symmetric PPI
ppi_data = pd.read_csv(ppi, sep='\t', header=None)
new_df = pd.DataFrame(data={
0: ppi_data[0],
1: ppi_data[1],
2: ppi_data[2]
})
ppi_data_symm = pd.concat([ppi_data, new_df])
panda_obj = Panda(expression,
motif_data,
ppi_data_symm,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#1. Union
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='union')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#2. In-degree and out-degree
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='union',
save_memory=False)
panda_obj.return_panda_indegree()
panda_obj.return_panda_outdegree()
# Lazy test
assert (np.round(panda_obj.panda_indegree.iloc[0].loc['force'],
5) == 1.13971)
assert (np.round(panda_obj.panda_outdegree.iloc[0].loc['force'],
5) == 1030.06840)
#3. Legacy
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess='legacy')
panda_obj.save_panda_results(output_file)
gt_file = 'tests/panda/legacy_test_panda.txt'
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
print('Test panda passed was successful!')
#4. None Types
i = 0
gt_test_panda = 'gt_panda'
test_panda = 'test_panda'
for modeProcess in ['legacy', 'union', 'intersection']:
print(modeProcess)
#Motif
i = i + 1
panda_obj = Panda(expression_data,
None,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#PPI
i = i + 1
panda_obj = Panda(expression_data,
motif,
None,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#Expression
i = i + 1
panda_obj = Panda(None,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#Expression and PPI
i = i + 1
panda_obj = Panda(None,
motif,
None,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
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!')