def NBS_single(sm_mat,
regNet_glap,
propNet=None,
propNet_kernel=None,
k=3,
verbose=False,
**kwargs):
# Check for correct input data
if type(sm_mat) != pd.DataFrame:
raise TypeError(
'Somatic mutation data must be given as Pandas DataFrame')
if propNet is not None:
if type(propNet) != nx.Graph:
raise TypeError('Networkx graph object required for propNet')
if regNet_glap is not None:
if type(regNet_glap) != pd.DataFrame:
raise TypeError(
'netNMF regularization network laplacian (regNet_glap) must be given as Pandas DataFrame'
)
# Load or set subsampling parameters
pats_subsample_p, gene_subsample_p, min_muts = 0.8, 0.8, 10
if 'pats_subsample_p' in kwargs:
pats_subsample_p = float(kwargs['pats_subsample_p'])
if 'gene_subsample_p' in kwargs:
gene_subsample_p = float(kwargs['gene_subsample_p'])
if 'min_muts' in kwargs:
min_muts = int(kwargs['min_muts'])
# Subsample Data
sm_mat_subsample = core.subsample_sm_mat(sm_mat,
propNet=propNet,
pats_subsample_p=pats_subsample_p,
gene_subsample_p=gene_subsample_p,
min_muts=min_muts)
if verbose:
print 'Somatic mutation data sub-sampling complete'
# Throw exception if subsampling returned empty dataframe
if sm_mat_subsample.shape[0] == 0:
raise ValueError(
'Subsampled somatic mutation matrix contains no patients.')
# Propagate data if network object is provided
if propNet is not None:
# Determine if propagation is can be based on pre-computed propagation kernel
if propNet_kernel is None:
# If kernel is not given and some propagation parameters are given in kwargs, set propagation parameters
# Otherwise set default values
alpha, symmetric_norm, save_prop = 0.7, False, False
if 'prop_alpha' in kwargs:
alpha = float(kwargs['prop_alpha'])
if 'prop_symmetric_norm' in kwargs:
symmetric_norm = ((kwargs['prop_symmetric_norm'] == 'True') |
(kwargs['prop_symmetric_norm'] == True))
if 'save_prop' in kwargs:
save_prop = ((kwargs['save_prop'] == 'True') |
(kwargs['save_prop'] == True))
# Save propagation step data if desired (indicated in kwargs)
if save_prop:
prop_sm_data = prop.network_propagation(
propNet,
sm_mat_subsample,
alpha=alpha,
symmetric_norm=symmetric_norm,
**kwargs)
else:
prop_sm_data = prop.network_propagation(
propNet,
sm_mat_subsample,
alpha=alpha,
symmetric_norm=symmetric_norm)
else:
# Save propagation step data if desired (indicated in kwargs)
save_prop = False
if 'save_prop' in kwargs:
save_prop = ((kwargs['save_prop'] == 'True') |
(kwargs['save_prop'] == True))
if save_prop:
prop_sm_data = prop.network_kernel_propagation(
propNet, propNet_kernel, sm_mat_subsample, **kwargs)
else:
prop_sm_data = prop.network_kernel_propagation(
propNet, propNet_kernel, sm_mat_subsample)
if verbose:
print 'Somatic mutation data propagated'
else:
prop_sm_data = sm_mat_subsample
if verbose:
print 'Somatic mutation data not propagated'
# Quantile Normalize Data
qnorm_data = True
if 'qnorm_data' in kwargs:
qnorm_data = ((kwargs['qnorm_data'] == 'True') |
(kwargs['qnorm_data'] == True))
if qnorm_data:
prop_data_qnorm = core.qnorm(prop_sm_data)
if verbose:
print 'Somatic mutation data quantile normalized'
else:
prop_data_qnorm = prop_sm_data
if verbose:
print 'Somatic mutation data not quantile normalized'
# Prepare data for mixed netNMF function (align propagated profile columns with regularization network laplacian rows)
if propNet is not None:
propNet_nodes = list(propNet.nodes)
data_arr = np.array(prop_data_qnorm.T.ix[propNet_nodes])
regNet_glap_arr = np.array(
regNet_glap.ix[propNet_nodes][propNet_nodes])
else:
propNet_nodes = list(regNet_glap.index)
data_arr = np.array(prop_data_qnorm.T.ix[propNet_nodes].fillna(0))
regNet_glap_arr = np.array(regNet_glap)
# Set netNMF parameters from kwargs if given, otherwise use defaults
netNMF_lambda, netNMF_maxiter, netNMF_verbose = 200, 250, False
netNMF_eps, netNMF_err_tol, netNMF_err_delta_tol = 1e-15, 1e-4, 1e-8
if 'netNMF_lambda' in kwargs:
netNMF_lambda = float(kwargs['netNMF_lambda'])
if 'netNMF_maxiter' in kwargs:
netNMF_maxiter = int(kwargs['netNMF_maxiter'])
if 'netNMF_eps' in kwargs:
netNMF_eps = float(kwargs['netNMF_eps'])
if 'netNMF_err_tol' in kwargs:
netNMF_err_tol = float(kwargs['netNMF_err_tol'])
if 'netNMF_err_delta_tol' in kwargs:
netNMF_err_delta_tol = float(kwargs['netNMF_err_delta_tol'])
# Mixed netNMF Result
W, H, numIter, finalResid = core.mixed_netNMF(
data_arr,
regNet_glap_arr,
k=k,
l=netNMF_lambda,
maxiter=netNMF_maxiter,
eps=netNMF_eps,
err_tol=netNMF_err_tol,
err_delta_tol=netNMF_err_delta_tol,
verbose=False)
# Return netNMF result (dimension-reduced propagated patient profiles)
H_df = pd.DataFrame(H.T, index=prop_data_qnorm.index)
# Save netNMF result
# Saving the propagation result
if 'outdir' in kwargs:
if 'job_name' in kwargs:
if 'iteration_label' in kwargs:
save_path = kwargs['outdir'] + str(
kwargs['job_name']) + '_H_' + str(
kwargs['iteration_label']) + '.csv'
else:
save_path = kwargs['outdir'] + str(
kwargs['job_name']) + '_H.csv'
else:
if 'iteration_label' in kwargs:
save_path = kwargs['outdir'] + 'H_' + str(
kwargs['iteration_label']) + '.csv'
else:
save_path = kwargs['outdir'] + 'H.csv'
H_df.to_csv(save_path)
if verbose:
print 'H matrix saved:', save_path
else:
pass
if verbose:
print 'pyNBS iteration complete'
return H_df
def NBS_single(sm_mat,
options,
propNet=None,
propNet_kernel=None,
regNet_glap=None,
verbose=True,
save_path=None):
# Set default NBS netNMF options
NBS_options = {
'pats_subsample_p': 0.8,
'gene_subsample_p': 0.8,
'min_muts': 10,
'prop_data': True,
'prop_alpha': 0.7,
'prop_symmetric_norm': False,
'qnorm_data': True,
'netNMF_k': 4,
'netNMF_gamma': 200,
'netNMF_update_gamma': False,
'netNMF_gamma_factor': 1,
'netNMF_niter': 250,
'netNMF_eps': 1e-15,
'netNMF_err_tol': 1e-4,
'netNMF_err_delta_tol': 1e-4
}
# Update NBS netNMF options
for option in options:
NBS_options[option] = options[option]
if verbose:
print 'NBS options set:'
for option in NBS_options:
print '\t', option + ':', NBS_options[option]
# Check for correct input data
if NBS_options['prop_data']:
if type(propNet) != nx.Graph:
raise TypeError('Networkx graph object required for propNet')
if (NBS_options['netNMF_gamma'] != 0):
if type(regNet_glap) != pd.DataFrame:
raise TypeError(
'netNMF regularization network laplacian (regNet_glap) must be given as Pandas DataFrame'
)
# Subsample Data
sm_mat_subsample = core.subsample_sm_mat(
sm_mat,
propNet=propNet,
pats_subsample_p=NBS_options['pats_subsample_p'],
gene_subsample_p=NBS_options['gene_subsample_p'],
min_muts=NBS_options['min_muts'])
if verbose:
print 'Somatic mutation data sub-sampling complete'
# Propagate Data
if NBS_options['prop_data']:
if propNet_kernel is None:
prop_sm_data = prop.network_propagation(
propNet,
sm_mat_subsample,
symmetric_norm=NBS_options['prop_symmetric_norm'],
alpha=NBS_options['prop_alpha'],
verbose=verbose)
else:
prop_sm_data = prop.network_kernel_propagation(propNet,
propNet_kernel,
sm_mat_subsample,
verbose=verbose,
save_path=None)
if verbose:
print 'Somatic mutation data propagated'
else:
prop_sm_data = sm_mat_subsample
print 'Somatic mutation data not propagated'
# Quantile Normalize Data
if NBS_options['qnorm_data']:
prop_data_qnorm = core.qnorm(prop_sm_data)
if verbose:
print 'Somatic mutation data quantile normalized'
else:
prop_data_qnorm = prop_sm_data
print 'Somatic mutation data not quantile normalized'
# Prepare data for mixed netNMF function
propNet_nodes = propNet.nodes()
data_arr = np.array(prop_data_qnorm.T.ix[propNet_nodes])
regNet_glap_arr = np.array(regNet_glap.ix[propNet_nodes][propNet_nodes])
# Mixed netNMF Result
W, H, numIter, finalResid = core.mixed_netNMF(
data_arr,
regNet_glap_arr,
NBS_options['netNMF_k'],
W_init=None,
H_init=None,
gamma=NBS_options['netNMF_gamma'],
update_gamma=NBS_options['netNMF_update_gamma'],
gamma_factor=NBS_options['netNMF_gamma_factor'],
niter=NBS_options['netNMF_niter'],
eps=NBS_options['netNMF_eps'],
err_tol=NBS_options['netNMF_err_tol'],
err_delta_tol=NBS_options['netNMF_err_delta_tol'],
verbose=verbose,
debug_mode=False)
# Save netNMF Result
H_df = pd.DataFrame(H.T, index=prop_data_qnorm.index)
if save_path is not None:
H_df.to_csv(save_path)
if verbose:
print 'netNMF result saved:', save_path
return H_df