def METHOD_NAME(self):
    self.DATA.tl.raw_checkpoint()
    self.DATA.tl.sctransform(do_correct_umi=True, filter_hvgs=True)
    data = self.DATA
    raw_matrix = data.tl.raw.exp_matrix
    counts_matrix = data.tl.result['sctransform'][0]['counts'].T
    data_matrix = data.tl.result['sctransform'][0]['data'].T
    scale_matrix = data.tl.result['sctransform'][0]['scale.data'].to_numpy().T
    main_matrix = data.exp_matrix
    # check `main_matrix` is `scale_matrix`, and default var_features_n is 3000
    self.assertEqual(scale_matrix.shape[1], 3000)
    self.assertEqual(main_matrix.shape, scale_matrix.shape)
    self.assertAlmostEqual(main_matrix[1].sum(), scale_matrix[1].sum(), places=4)
    write_h5ad(self.DATA, output=TEST_DATA_PATH + "135.sct.h5ad")
    _ = read_stereo_h5ad(TEST_DATA_PATH + "135.sct.h5ad")
    ann_data = stereo_to_anndata(self.DATA)
    ann_raw_matrix = ann_data.raw.X
    ann_main_matrix = ann_data.X
    # check `ann_raw_matrix` is `scale_matrix`
    self.assertEqual(ann_raw_matrix.shape, raw_matrix.shape)
    self.assertAlmostEqual(ann_raw_matrix[1].sum(), raw_matrix[1].sum(), places=4)
    # check `ann_main_matrix` is `scale_matrix`
    self.assertEqual(ann_main_matrix.shape, scale_matrix.shape)
    self.assertAlmostEqual(ann_main_matrix[1].sum(), scale_matrix[1].sum(), places=4)