def METHOD_NAME(self):
    data = st.io.read_gef(self.gef_file)
    data.tl.cal_qc()
    data.plt.violin(out_path=TEST_IMAGE_PATH + "violin.png")
    data.plt.spatial_scatter(out_path=TEST_IMAGE_PATH + "spatial_scatter.png")
    data.plt.genes_count(out_path=TEST_IMAGE_PATH + "genes_count.png")
    data.tl.raw_checkpoint()
    data.tl.normalize_total(target_sum=1e4)
    data.tl.log1p()
    data.tl.highly_variable_genes(
        min_mean=0.0125, max_mean=3, min_disp=0.5, res_key='highly_variable_genes', n_top_genes=None
    )
    data.plt.highly_variable_genes(res_key='highly_variable_genes', out_path=TEST_IMAGE_PATH + "highly_variable_genes.png")
    data.tl.scale(zero_center=False)
    data.tl.pca(
        use_highly_genes=True, hvg_res_key='highly_variable_genes', n_pcs=20, res_key='pca', svd_solver='arpack'
    )
    data.tl.neighbors(pca_res_key='pca', n_pcs=30, res_key='neighbors', n_jobs=2)
    data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap', init_pos='spectral')
    data.tl.leiden(neighbors_res_key='neighbors', res_key='leiden')
    data.plt.umap(res_key='umap', cluster_key='leiden', out_path=TEST_IMAGE_PATH + "umap.png")
    data.plt.cluster_scatter(res_key='leiden', out_path=TEST_IMAGE_PATH + "leiden.png")