regressor_kwargs=method_params[1],
tf_matrix=tf_matrix,
tf_matrix_gene_names=tf_matrix_gene_names,
target_gene_name=target_gene_name,
target_gene_expression=target_gene_expression,
include_meta=False,
early_stop_window_length=EARLY_STOP_WINDOW_LENGTH,
seed=args.seed)
return (n)
if __name__ == '__main__':
start_time = time.time()
ex_matrix = load_exp_matrix(args.expression_mtx_fname,
(args.transpose == 'yes'), args.sparse,
args.cell_id_attribute, args.gene_attribute)
if args.sparse:
gene_names = ex_matrix[1]
ex_matrix = ex_matrix[0]
else:
gene_names = ex_matrix.columns
end_time = time.time()
print(
f'Loaded expression matrix of {ex_matrix.shape[0]} cells and {ex_matrix.shape[1]} genes in {end_time - start_time} seconds...',
file=sys.stdout)
tf_names = load_tf_names(args.tfs_fname)
print(f'Loaded {len(tf_names)} TFs...', file=sys.stdout)
def convert(fname_csv, fname_loom, transpose):
df = load_exp_matrix(fname_csv, transpose)
save_df_as_loom(df, fname_loom)