My input file is 04.tissue.gef and I ran the following code,
data = st.io.read_gef("/mnt/strna/stomics/sample/1011_test/tissu_gef/04.tissue.gef", bin_size=50) data.tl.cal_qc() data.tl.filter_cells(min_gene=1) data.tl.raw_checkpoint() data.tl.normalize_total() data.tl.log1p() data.tl.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5, n_top_genes=3000, res_key='highly_variable_genes') data.tl.scale(zero_center=False) data.tl.pca(use_highly_genes=False, hvg_res_key='highly_variable_genes', n_pcs=20, res_key='pca') data.tl.neighbors(pca_res_key='pca', n_pcs=30, res_key='neighbors') data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap') data.tl.leiden(neighbors_res_key='neighbors', res_key='leiden')
My input file is 04.tissue.gef and I ran the following code,
data = st.io.read_gef("/mnt/strna/stomics/sample/1011_test/tissu_gef/04.tissue.gef", bin_size=50) data.tl.cal_qc() data.tl.filter_cells(min_gene=1) data.tl.raw_checkpoint() data.tl.normalize_total() data.tl.log1p() data.tl.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5, n_top_genes=3000, res_key='highly_variable_genes') data.tl.scale(zero_center=False) data.tl.pca(use_highly_genes=False, hvg_res_key='highly_variable_genes', n_pcs=20, res_key='pca') data.tl.neighbors(pca_res_key='pca', n_pcs=30, res_key='neighbors') data.tl.umap(pca_res_key='pca', neighbors_res_key='neighbors', res_key='umap') data.tl.leiden(neighbors_res_key='neighbors', res_key='leiden')then, I got umap result like this
but, the result of SAWtools is
May I ask how to reproduce this result?