def helper_genie3(X, theta_true, tf_names=[], BEELINE=False): #_string
print('Running GENIE3 method', X.shape)
theta_true = theta_true.real
ex_matrix = pd.DataFrame(X)
if args.USE_TF_NAMES == 'yes' and len(tf_names) != 0:
tf_names = ['G' + str(n) for n in tf_names]
else:
tf_names = None
gene_names = ['G' + str(c) for c in ex_matrix.columns]
ex_matrix.columns = gene_names
network = genie3(expression_data=ex_matrix,
gene_names=gene_names,
tf_names=tf_names) #, verbose=True)
pred_edges = np.array(network[['TF', 'target', 'importance']])
G_pred = nx.Graph()
# G_pred.add_nodes_from(['G'+str(n) for n in range(args.D)])
G_pred.add_nodes_from(['G' + str(n) for n in range(len(gene_names))])
G_pred.add_weighted_edges_from(pred_edges)
# pred_theta = nx.adj_matrix(G_pred).todense() + np.eye(args.D)
pred_theta = nx.adj_matrix(G_pred).todense() + np.eye(len(gene_names))
recovery_metrics = report_metrics(np.array(theta_true),
np.array(pred_theta))
print(
'GENIE3: FDR, TPR, FPR, SHD, nnz_true, nnz_pred, precision, recall, Fb, aupr, auc'
)
print('GENIE3: Recovery of true theta: ', *np.around(recovery_metrics, 3))
res = list(recovery_metrics)
if BEELINE:
res = [list(recovery_metrics), pred_theta]
return res
def main(args):
opts, args = parseArgs(args)
inDF = pd.read_csv(opts.inFile, sep='\t', index_col=0, header=0)
client = Client(processes=False)
if opts.algo == 'GENIE3':
network = genie3(inDF, client_or_address=client)
network.to_csv(opts.outFile, index=False, sep='\t')
elif opts.algo == 'GRNBoost2':
network = grnboost2(inDF, client_or_address=client)
network.to_csv(opts.outFile, index=False, sep='\t')
else:
print("Wrong algorithm name. Should either be GENIE3 or GRNBoost2.")
output_grnboost2=cell_use+'_grnboost2.csv'
output_genie3=cell_use+'_genie3.csv'
output_grnboost2_txt=cell_use+'_grnboost2.txt'
output_genie3_txt=cell_use+'_genie3.txt'
#load data
ex_matrix=pd.read_csv(input_file,sep=',')
ex_matrix=np.transpose(ex_matrix)
tf_names=load_tf_names('mm_mgi_tfs.txt')
#infer the gene regulatory network
network_n=grnboost2(ex_matrix, tf_names=tf_names, verbose=True)
network_g=genie3(ex_matrix, tf_names=tf_names, verbose=True)
#for following igraph analysis
network_n.to_csv(output_grnboost2,sep='\t')
network_g.to_csv(output_genie3,sep='\t')
#txt file with no header and index for FAC calculation
network_n.to_csv(output_grnboost2_txt,sep='\t',header=False,index=False)
network_g.to_csv(output_genie3_txt,sep='\t',header=False,index=False)
####### done!
def test_launch_genie3(self):
network_df = genie3(df, tf_names=tfs)
self.assertGreater(len(network_df), 100)
def run_genie3(Expr, filename='links.txt', gene_names=None, **kwargs):
links = genie3(np.asmatrix(Expr.T), gene_names=gene_names, **kwargs)
links.to_csv(filename, sep='\t', index=False, header=False)