scanpy处理：使用自定义 python 函数读取百迈客空间转录组数据（百创智造S1000）

原本的版本用的怪怪的，定义了一个函数方便直接在解释器中使用。函数返回adata对象，示例代码如下：

def read_bmk(input, input_png, library_id):import cv2import osadata = sc.read_10x_mtx(input, var_names='gene_symbols',cache=False)barcode_pos = pd.read_csv(os.path.join(input, "barcodes_pos.tsv.gz"), compression='gzip', sep='\t', names=["barcode","array_col","array_row"],header=None)barcode_pos['in_tissue']=1barcode_pos=barcode_pos[['barcode','in_tissue','array_row','array_col']]barcode_pos.index = barcode_pos['barcode']barcode_pos = barcode_pos.loc[adata.obs_names, :] adata.obs=barcode_posadata.obs.index = adata.obs['barcode'].to_list()adata.obs=adata.obs[['in_tissue','array_row','array_col']]obsm=barcode_pos[['array_col','array_row']]obsm= obsm.to_numpy()adata.obsm["spatial"] = obsmhe_img = cv2.imread(input_png)he_img=he_img/255adata.uns["spatial"] = {library_id: {}}adata.uns["spatial"][library_id]["images"] = {}adata.uns['spatial'][library_id]['images']['hires'] = he_img.astype(np.float32)adata.uns['spatial'][library_id]['use_quality']='hires'def cal_zoom_rate(width, height):std_width = 1000std_height = std_width / (46 * 31) * (46 * 36 * np.sqrt(3) / 2.0)if std_width / std_height > width / height:scale = width / std_widthelse:scale = height / std_heightreturn scalezoom_scale = cal_zoom_rate(he_img.shape[1], he_img.shape[0])adata.uns['spatial'][library_id]['scalefactors']= {"spot_diameter_fullres": zoom_scale, "tissue_hires_scalef": zoom_scale,"fiducial_diameter_fullres": zoom_scale, "tissue_lowres_scalef": zoom_scale}sc.pl.spatial(adata, img_key="hires")return adata

使用示例，选择level7的标准输出创建看看:

adata = read_bmk(input = "./self_data_analysis/cancer1/L7_heAuto",input_png="./self_data_analysis/cancer1/he_roi_small.png",library_id="cancer1")