Cellxgene Census 常用模式

# 常见查询模式与最佳实践 ## 查询模式分类 ### 1. 探索性查询（仅元数据） 在不加载表达矩阵的情况下探索可用数据时使用。 **模式：获取组织中唯一的细胞类型** python import cellxgene_census with cellxgene_census.open_soma() as census: cell_metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="tissue_general == 'brain' and is_primary_data == True", column_names=["cell_type"] ) unique_cell_types = cell_metadata["cell_type"].unique() print(f"发现 {len(unique_cell_types)} 种独特的细胞类型") **模式：按条件统计细胞数量** python cell_metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="disease != 'normal' and is_primary_data == True", column_names=["disease", "tissue_general"] ) counts = cell_metadata.groupby(["disease", "tissue_general"]).size() **模式：探索数据集信息** python # 访问数据集表 datasets = census["census_info"]["datasets"].read().concat().to_pandas() # 按特定条件过滤 covid_datasets = datasets[datasets["disease"].str.contains("COVID", na=False)] ### 2. 中小规模查询 (AnnData) 当结果适合内存（通常 < 10万个细胞）时，使用 `get_anndata()`。 **模式：组织特异性细胞类型查询** python adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True", obs_column_names=["assay", "disease", "sex", "donor_id"], ) **模式：多基因特异性查询** python marker_genes = ["CD4", "CD8A", "CD19", "FOXP3"] # 首先获取基因 ID gene_metadata = cellxgene_census.get_var( census, "homo_sapiens", value_filter=f"feature_name in {marker_genes}", column_names=["feature_id", "feature_name"] ) gene_ids = gene_metadata["feature_id"].tolist() # 使用基因过滤器进行查询 adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", var_value_filter=f"feature_id in {gene_ids}", obs_value_filter="cell_type == 'T cell' and is_primary_data == True", ) **模式：多组织查询** python adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="tissue_general in ['lung', 'liver', 'kidney'] and is_primary_data == True", obs_column_names=["