INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

Yuyao Liu, Zhen Li, Xiaoyang Chen, Xuejian Cui, Zijing Gao and Rui Jiang ()
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Yuyao Liu: Tsinghua University
Zhen Li: Tsinghua University
Xiaoyang Chen: Tsinghua University
Xuejian Cui: Tsinghua University
Zijing Gao: Tsinghua University
Rui Jiang: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-23

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-56535-0

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