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import scanpy as scimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snssc.logging.print_versions()sc.set_figure_params(facecolor="white", figsize=(8, 8))sc.settings.verbosity = 3
adata = sc.datasets.visium_sge(sample_id="V1_Human_Lymph_Node")adata.var_names_make_unique()adata.var["mt"] = adata.var_names.str.startswith("MT-")sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], inplace=True)adata
fig, axs = plt.subplots(1, 4, figsize=(15, 4))sns.histplot(adata.obs["total_counts"], kde=False, ax=axs[0])sns.histplot(adata.obs["total_counts"][adata.obs["total_counts"] < 10000],kde=False,bins=40,ax=axs[1],)sns.histplot(adata.obs["n_genes_by_counts"], kde=False, bins=60, ax=axs[2])sns.histplot(adata.obs["n_genes_by_counts"][adata.obs["n_genes_by_counts"] < 4000],kde=False,bins=60,ax=axs[3],)
sc.pp.filter_cells(adata, min_counts=5000)sc.pp.filter_cells(adata, max_counts=35000)adata = adata[adata.obs["pct_counts_mt"] < 20].copy()print(f"#cells after MT filter: {adata.n_obs}")sc.pp.filter_genes(adata, min_cells=10)
sc.pp.normalize_total(adata, inplace=True)sc.pp.log1p(adata)sc.pp.highly_variable_genes(adata, flavor="seurat", n_top_genes=2000)
sc.pp.pca(adata)sc.pp.neighbors(adata)sc.tl.umap(adata)sc.tl.leiden(adata, key_added="clusters", flavor="igraph", directed=False, n_iterations=2)
plt.rcParams["figure.figsize"] = (4, 4)sc.pl.umap(adata, color=["total_counts", "n_genes_by_counts", "clusters"], wspace=0.4)
plt.rcParams["figure.figsize"] = (8, 8)sc.pl.spatial(adata, img_key="hires", color=["total_counts", "n_genes_by_counts"])
sc.pl.spatial(adata, img_key="hires", color="clusters", size=1.5)sc.pl.spatial(adata,img_key="hires",color="clusters",groups=["5", "9"],crop_coord=[7000, 10000, 0, 6000],alpha=0.5,size=1.3,)
sc.tl.rank_genes_groups(adata, "clusters", method="t-test")sc.pl.rank_genes_groups_heatmap(adata, groups="9", n_genes=10, groupby="clusters")
##我们看到 CR2 概括了空间结构。sc.pl.spatial(adata, img_key="hires", color=["clusters", "CR2"])
# If needed:# %pip install openpyxlcoordinates = pd.read_excel("./data/pnas.1912459116.sd15.xlsx", index_col=0)counts = sc.read_csv("./data/pnas.1912459116.sd12.csv").transpose()adata_merfish = counts[coordinates.index, :].copy()adata_merfish.obsm["spatial"] = coordinates.to_numpy()#预处理和降维sc.pp.normalize_per_cell(adata_merfish, counts_per_cell_after=1e6)sc.pp.log1p(adata_merfish)sc.pp.pca(adata_merfish, n_comps=15)sc.pp.neighbors(adata_merfish)sc.tl.umap(adata_merfish)sc.tl.leiden(adata_merfish,key_added="clusters",resolution=0.5,n_iterations=2,flavor="igraph",directed=False,)adata_merfishsc.pl.umap(adata_merfish, color="clusters")sc.pl.embedding(adata_merfish, basis="spatial", color="clusters")
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