def __init__(self):
self._abbr = "fumia_2013"
self._name = "Fumiã et al. PLoS ONE, (2013) 8(7), e69008"
inputs = {}
inputs['Mutagen'] = 1.0
inputs['GFs'] = 1.0
inputs['Nutrients'] = 1.0
inputs['TNFα'] = 0.0
inputs['Hypoxia'] = 1.0
dpath = os.path.dirname(__file__)
fpath_network = os.path.join(dpath, 'network.sif')
A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True)
self._A = A
self._n2i = n2i
self._i2n = {idx: name for name, idx in n2i.items()}
self._dg = dg
self._inputs = inputs
# The following members are not defined due to the lack of data.
self._df_conds = None
self._df_exp = None
self._df_ptb = None
self._has_link_perturb = False
self._names_ptb = None
self._iadj_to_idf = None
def __init__(self, abbr, dpath, fpath_network):
self._abbr = abbr
self._name = "Korkut and Wang et al. eLife 2015;4:e04640"
fpath_ptb = os.path.join(dpath, "ptb.tsv")
A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True)
self._A = A
self._n2i = n2i
self._dg = dg
self._df_conds = pd.read_table(os.path.join(dpath, "conds.tsv"),
header=0,
index_col=0)
self._df_exp = pd.read_table(os.path.join(dpath, "exp.tsv"),
header=0,
index_col=0)
self._inputs = {}
self._df_ptb = pd.read_table(fpath_ptb, index_col=0)
if any(self._df_ptb.Type == 'link'):
self._has_link_perturb = True
else:
self._has_link_perturb = False
# Remove the rows and columns of a node which is not
# included in the given network structure.
not_included = set(self._df_conds.columns) - set(self._n2i.keys())
for target in not_included:
ind_removed = self._df_conds[self.df_conds[target] != 0].index
self._df_conds.drop(ind_removed, inplace=True)
self._df_conds.drop([target], axis=1, inplace=True)
self._df_exp.drop(ind_removed, inplace=True)
self._df_ptb.drop([target], inplace=True)
# Re-index according to the new size.
self._df_conds.index = np.arange(1, self._df_exp.shape[0] + 1)
self._df_exp.index = self._df_conds.index
self._names_ptb = []
for i, row in enumerate(self._df_conds.iterrows()):
row = row[1]
list_name = [] # Target names
for target in self._df_conds.columns[row.nonzero()]:
list_name.append(target)
# end of for
self._names_ptb.append(list_name)
# end of for
s1 = set(self._df_exp.columns) # From experimental data
s2 = set(n2i.keys()) # From network
exp_only = s1 - s2
self._df_exp.drop(exp_only, axis=1, inplace=True)
# For mapping from the indices of adj. matrix to those of DataFrame
# (arrange the indices of adj. matrix according to df_exp.columns)
self._iadj_to_idf = [n2i[x] for x in self._df_exp.columns]
self._i2n = {idx: name for name, idx in n2i.items()}
def __init__(self):
self._abbr = "FUMIA_2013"
self._name = ""
dpath = os.path.dirname(__file__)
dpath_network = os.path.join(dpath, "fumia_network.sif")
A, n2i, dg = sfa.read_sif(dpath_network, as_nx=True)
self._A = A
self._n2i = n2i
self._dg = dg
self.i2n = {idx: name for name, idx in self._n2i.items()}
def __init__(self):
super().__init__()
self._abbr = "TNC"
self._name = "A simple three node cascade"
# Specify the file path for network file in SIF.
dpath = os.path.dirname(__file__)
fpath = os.path.join(dpath, 'union.sif')
# Use read_sif function.
signs = {'pos_interaction':1, 'neg_interaction':-1}
A, n2i, dg = sfa.read_sif(fpath, signs=signs, as_nx=True)
self._A = A
self._n2i = n2i
self._dg = dg
self._i2n = {idx: name for name, idx in n2i.items()}
def __init__(self):
self._abbr = "flobak_2015"
self._name = "Flobak et al. PLoS Comput Biol, (2015) 11(8)"
inputs = {}
dpath = os.path.dirname(__file__)
fpath_network = os.path.join(dpath, 'network.sif')
A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True)
self._A = A
self._n2i = n2i
self._i2n = {idx: name for name, idx in n2i.items()}
self._dg = dg
self._inputs = inputs
# The following members are not defined due to the lack of data.
self._df_conds = None
self._df_exp = None
self._df_ptb = None
self._has_link_perturb = False
self._names_ptb = None
self._iadj_to_idf = None
def __init__(self):
self._abbr = "steinway_2015"
self._name = "Steinway et al. Npj Syst Biol Appl (2015) 1(1), 15014"
inputs = {}
inputs['TGFβ'] = 1.0
dpath = os.path.dirname(__file__)
fpath_network = os.path.join(dpath, 'network.sif')
A, n2i, dg = sfa.read_sif(fpath_network, as_nx=True)
self._A = A
self._n2i = n2i
self._i2n = {idx: name for name, idx in n2i.items()}
self._dg = dg
self._inputs = inputs
# The following members are not defined due to the lack of data.
self._df_conds = None
self._df_exp = None
self._df_ptb = None
self._has_link_perturb = False
self._names_ptb = None
self._iadj_to_idf = None