Chen Xiaoyang, Li Keyi, Wu Xiaoqing, Li Zhen, Jiang Qun, Cui Xuejian, Gao Zijing, Wu Yanhong, Jiang Rui
Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China.
Genome Biol. 2024 Dec 30;25(1):322. doi: 10.1186/s13059-024-03458-6.
Spatial epigenomic technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here, we propose an efficient and iterative model, Descart, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking, we demonstrate the superiority of Descart in revealing cellular heterogeneity and capturing tissue structure. Utilizing the graph of inter-cellular correlations, Descart shows its potential to denoise data, identify peak modules, and detect gene-peak interactions.
空间表观基因组技术能够同时捕获组织切片内细胞的空间位置和染色质可及性。识别显示空间变异和细胞异质性的峰是表征复杂组织空间染色质可及性景观的关键分析任务。在这里，我们提出了一种高效的迭代模型Desct，用于基于细胞间相关性图进行空间可变峰识别。通过全面的基准测试，我们证明了Desct在揭示细胞异质性和捕获组织结构方面的优越性。利用细胞间相关性图，Desct显示了其在数据去噪、识别峰模块和检测基因-峰相互作用方面的潜力。
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