ex_matrix = pd.DataFrame(data=ds[:, :],
index=ds.ra[args.gene_attribute],
columns=ds.ca[args.cell_id_attribute]).T
gene_names = pd.Series(ds.ra[args.gene_attribute])
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.stderr)
tf_names = load_tf_names(args.tfs_fname.name)
print(f'Loaded {len(tf_names)} TFs...', file=sys.stderr)
ex_matrix, gene_names, tf_names = _prepare_input(ex_matrix, gene_names,
tf_names)
tf_matrix, tf_matrix_gene_names = to_tf_matrix(ex_matrix, gene_names,
tf_names)
print(f'starting {args.method} using {args.num_workers} processes...',
file=sys.stderr)
start_time = time.time()
with Pool(args.num_workers) as p:
adjs = list(
tqdm.tqdm(p.imap(run_infer_partial_network,
target_gene_indices(gene_names,
target_genes='all'),
chunksize=1),
total=len(gene_names)))
adj = pd.concat(adjs).sort_values(by='importance', ascending=False)
lf = lp.connect(args.expression_mtx_fname.name, mode='r', validate=False)
# genes in columns:
ex_matrix = pd.DataFrame(lf[:, :], index=lf.ra.Gene,
columns=lf.ca.CellID).T
lf.close()
gene_names = ex_matrix.columns
print('Loaded expression matrix of {} cells and {} genes...'.format(
ex_matrix.shape[0], ex_matrix.shape[1]),
file=sys.stderr)
tf_names = load_tf_names(args.tfs_fname.name)
print('Loaded {} TFs...'.format(len(tf_names)), file=sys.stderr)
expression_matrix, gene_names, tf_names = _prepare_input(
ex_matrix, gene_names, tf_names)
tf_matrix, tf_matrix_gene_names = to_tf_matrix(expression_matrix,
gene_names, tf_names)
print('starting GRNBoost2 using {} processes...'.format(args.num_workers),
file=sys.stderr)
start_time = time.time()
with Pool(args.num_workers) as p:
adjs = p.map(runInferPartialNet,
target_gene_indices(gene_names, target_genes='all'))
adj = pd.concat(adjs).sort_values(by='importance', ascending=False)
end_time = time.time()
print('Done in {} seconds.'.format(end_time - start_time), file=sys.stderr)
adj.to_csv(args.output, index=False, sep="\t")