def test_all(self):
rawData = test_data.rawData
_ = deepImpute(rawData, ncores=4, NN_lim=2000)
def test_all(self, mock_args):
_ = deepImpute()
def test_minExpressionLevel(self):
_ = deepImpute(test_data.rawData, ncores=4, minVMR=1)
parser = argparse.ArgumentParser()
parser.add_argument('--loom', required=True, type=str, help="Loom")
parser.add_argument('--ncores', required=False, type=int, default=16, help="Number of cores.")
parser.add_argument('--min-expressed-cell', required=False, type=int, default=10, help="min-expressed-cell")
parser.add_argument("--min-expressed-cell-average-expression", required=False, type=float, default=1, help="min-expressed-cell-average-expression")
FLAGS = vars(parser.parse_args())
output_dir = "{}/imputation".format(FLAGS["loom"].rsplit("/", 1)[0])
os.makedirs(output_dir, exist_ok=True)
starttime = time.time()
gene_bc_mat, cell_id, gene_name = read_loom(FLAGS["loom"])
min_expressed_cell = FLAGS["min_expressed_cell"]
min_expressed_cell_average_expression = FLAGS["min_expressed_cell_average_expression"]
expressed_cell = (gene_bc_mat > 0).sum(1)
gene_expression = gene_bc_mat.sum(1)
gene_filter = np.bitwise_and(expressed_cell >= min_expressed_cell, gene_expression > expressed_cell * min_expressed_cell_average_expression)
input_gene_bc_mat = gene_bc_mat[gene_filter, :]
print(input_gene_bc_mat.shape)
# dimension = (cells x genes)
input_pd = pd.DataFrame(input_gene_bc_mat).T
imputed = deepImpute(input_pd, NN_lim='auto', ncores=FLAGS["ncores"], cell_subset=1)
input_loom_name = FLAGS["loom"].rsplit("/", 1)[1]
output_h5 = input_loom_name.replace(".loom", "_DeepImpute_mc_{}_mce_{}.hdf5".format(min_expressed_cell, min_expressed_cell_average_expression))
with h5py.File("{}/{}".format(output_dir, output_h5), "w") as f:
f["cell_id"] = cell_id.astype(h5py.special_dtype(vlen=str))
f["gene_name"] = gene_name[gene_filter].astype(h5py.special_dtype(vlen=str))
if_dset_imputation = f.create_dataset("imputation", shape=(cell_id.size, gene_filter.sum()), chunks=(1, gene_filter.sum()), dtype=np.float32)
if_dset_imputation[...] = imputed.values
print(pd.to_timedelta(time.time() - starttime, unit="s"))
def test_all(self):
_ = deepImpute(test_data.rawData, NN_lim=1000)