Liu Yuyao, Li Zhen, Chen Xiaoyang, Cui Xuejian, Gao Zijing, 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.
Nat Commun. 2025 Feb 1;16(1):1247. doi: 10.1038/s41467-025-56535-0.
Recent advances in spatial epigenomic techniques have given rise to spatial assay for transposase-accessible chromatin using sequencing (spATAC-seq) data, enabling the characterization of epigenomic heterogeneity and spatial information simultaneously. Integrative analysis of multiple spATAC-seq samples, for which no method has been developed, allows for effective identification and elimination of unwanted non-biological factors within the data, enabling comprehensive exploration of tissue structures and providing a holistic epigenomic landscape, thereby facilitating the discovery of biological implications and the study of regulatory processes. In this article, we present INSTINCT, a method for multi-sample INtegration of Spatial chromaTIN accessibility sequencing data via stochastiC domain Translation. INSTINCT can efficiently handle the high dimensionality of spATAC-seq data and eliminate the complex noise and batch effects of samples through a stochastic domain translation procedure. We demonstrate the superiority and robustness of INSTINCT in integrating spATAC-seq data across multiple simulated scenarios and real datasets. Additionally, we highlight the advantages of INSTINCT in spatial domain identification, visualization, spot-type annotation, and various downstream analyses, including motif enrichment analysis, expression enrichment analysis, and partitioned heritability analysis.
空间表观基因组技术的最新进展催生了利用测序技术进行转座酶可及染色质的空间分析(spATAC-seq)数据,从而能够同时表征表观基因组异质性和空间信息。对于多个spATAC-seq样本,尚未开发出整合分析方法,而对其进行整合分析可以有效识别和消除数据中不需要的非生物学因素,全面探索组织结构并提供整体表观基因组景观,从而促进生物学意义的发现和调控过程的研究。在本文中,我们介绍了INSTINCT,这是一种通过随机域转换对空间染色质可及性测序数据进行多样本整合的方法。INSTINCT可以有效处理spATAC-seq数据的高维度,并通过随机域转换过程消除样本的复杂噪声和批次效应。我们展示了INSTINCT在整合多个模拟场景和真实数据集的spATAC-seq数据方面的优越性和稳健性。此外,我们强调了INSTINCT在空间域识别、可视化、斑点类型注释以及各种下游分析(包括基序富集分析、表达富集分析和分区遗传力分析)中的优势。