多样本整合Banksy空间聚类分析(Visium HD, Xenium, CosMx)

news2025/5/13 8:03:29

在空间数据分析中，传统的单细胞聚类算法，例如Seurat和Scanpy中的lovain和leiden等聚类算法，通常在处理空间数据时忽略了空间信息。然而，由于细胞状态受其周围细胞的影响，将转录组数据与细胞的空间信息结合起来进行聚类分析，有助于揭示细胞在组织中的分布和相互作用。之前我们介绍了Banksy的python版本代码使用（针对单个样本）。我们知道BANKSY是一种空间组学数据的聚类方法，通过将每个细胞的特征(基因表达)与其空间近邻特征的平均值以及邻域特征梯度相结合来进行聚类，这样就结合了细胞分子邻域特征，增强了空间结构识别。

Visium HD数据分析之空间聚类算法Banksy

这里我们介绍下针对有多个样本情况下，从一个包含多个样本细胞的Anndata对象开始，先对多样本空间坐标进行错位处理（Staggering，解决多个样本合并时坐标重叠问题），然后怎样构建空间最近邻图，再降维和harmony去批次，最后整合起来进行聚类。

1. 未经坐标偏移的两个样本空间坐标（原始）

2. Stagger the spatial coordinates across the samples, so that spots from different samples do not overlap (sample specific treatment)

3. Calculate Banksy matrix

4. 多样本Banksy后空间及UMAP结果

代码实现：

分析环境加载

import osimport umapimport randomimport numpy as npimport pandas as pdimport scanpy as scfrom harmony import harmonizefrom banksy.initialize_banksy import initialize_banksyfrom banksy.embed_banksy import generate_banksy_matrixfrom banksy.main import concatenate_allfrom banksy_utils.umap_pca import pca_umapfrom banksy.cluster_methods import run_Leiden_partitionimport matplotlib.pyplot as pltimport warningswarnings.filterwarnings("ignore")plt.style.use('default')plt.rcParams['figure.facecolor'] = 'white'seeds = [0]random.seed(seeds[0])np.random.seed(seeds[0])

多样本数据读取及预处理

adata = sc.read_h5ad('/data/VisiumHD/CRC_demo/adata_raw.h5ad')
# 这里只是为了快速演示，所以抽了百分之十细胞进行后续分析sc.pp.sample(adata, 0.1)
adata.var["mt"] = adata.var_names.str.startswith("MT-")adata.var["ribo"] = adata.var_names.str.startswith(("RPS", "RPL"))sc.pp.calculate_qc_metrics(adata, qc_vars=["mt", "ribo"], inplace=True, log1p=True)sc.pp.filter_cells(adata, min_counts=30)sc.pp.filter_genes(adata, min_cells=10)adata = adata[adata.obs['pct_counts_mt']<20, :]# Normalizeadata.layers["counts"] = adata.X.copy()sc.pp.normalize_total(adata)

Stagger the spatial coordinates across the samples, so that spots from different samples do not overlap (sample specific treatment)

adata.obs['x_pixel'] = adata.obsm['spatial'][:, 0]adata.obs['y_pixel'] = adata.obsm['spatial'][:, 1]# 为每个样本生成颜色unique_samples = adata.obs['sample'].unique()colors = plt.cm.tab20(np.linspace(0, 1, len(unique_samples)))color_dict = {sample: colors[i] for i, sample in enumerate(unique_samples)}sample_colors = [color_dict[s] for s in adata.obs['sample']]# 绘制散点图fig = plt.figure(figsize=(6, 4), constrained_layout=True)scatter = plt.scatter(    x=adata.obs['x_pixel'],    y=adata.obs['y_pixel'],    c=sample_colors,    alpha=1,    s=1.5)
# Staggeringcoords_df = pd.DataFrame(adata.obs[['x_pixel', 'y_pixel', 'sample']])coords_df['x_pixel'] = coords_df.groupby('sample')['x_pixel'].transform(lambda x: x - x.min())global_max_x = max(coords_df['x_pixel']) * 1.5#Turn samples into factorscoords_df['sample_no'] = pd.Categorical(coords_df['sample']).codes#Update x coordinatescoords_df['x_pixel'] = coords_df['x_pixel'] + coords_df['sample_no'] * global_max_x#Update staggered coords to AnnData objectadata.obs['x_pixel'] = coords_df['x_pixel']adata.obs['y_pixel'] = coords_df['y_pixel']# AFTER staggering the spatial coordinatesfig = plt.figure(figsize=(6, 4), constrained_layout=True)scatter = plt.scatter(    x=adata.obs['x_pixel'],    y=adata.obs['y_pixel'],    c=sample_colors,    alpha=1,    s=1.5)

Subset to 2000 HVGs and Running BANKSY (with AGF)

sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor='seurat_v3', layer='counts', batch_key="sample")adata = adata[:, adata.var['highly_variable']]
## banksy parameters ##nbr_weight_decay = 'scaled_gaussian'k_geom = 18resolutions = [0.40]       # clustering resolution for UMAPpca_dims = [20]            # Dimensionality in which PCA reduces tolambda_list = [0.2]        # list of lambda parametersm = 1c_map =  'tab20' # specify color map# Include spatial coordinates informationcoord_keys = ('x_pixel', 'y_pixel', 'coord_xy')x_coord, y_coord, xy_coord = coord_keys[0], coord_keys[1], coord_keys[2]raw_y, raw_x = adata.obs[y_coord], adata.obs[x_coord]adata.obsm[xy_coord] = np.vstack((adata.obs[x_coord].values, adata.obs[y_coord].values)).T### run banksy ###banksy_dict = initialize_banksy(    adata,    coord_keys,    k_geom,    nbr_weight_decay=nbr_weight_decay,    max_m=m,    plt_edge_hist= True,    plt_nbr_weights= True,    plt_agf_angles=False,    plt_theta=False)banksy_dict, banksy_matrix = generate_banksy_matrix(    adata,    banksy_dict,    lambda_list,    max_m=m)banksy_dict["nonspatial"] = {    # Here we simply append the nonspatial matrix (adata.X) to obtain the nonspatial clustering results    0.0: {"adata": concatenate_all([adata.X], 0, adata=adata), }}print(banksy_dict['nonspatial'][0.0]['adata'])