Detecting anomalous anatomic regions in spatial transcriptomics with STANDS

Kaichen Xu, Yan Lu, Suyang Hou, Kainan Liu, Yihang Du, Mengqian Huang, Hao Feng, Hao Wu and Xiaobo Sun ()
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Kaichen Xu: Zhongnan University of Economics and Law
Yan Lu: Zhongnan University of Economics and Law
Suyang Hou: Zhongnan University of Economics and Law
Kainan Liu: The Hong Kong University of Science and Technology (Guangzhou)
Yihang Du: Zhongnan University of Economics and Law
Mengqian Huang: Zhongnan University of Economics and Law
Hao Feng: Case Western Reserve University
Hao Wu: Chinese Academy of Sciences, Shenzhen
Xiaobo Sun: Zhongnan University of Economics and Law

Nature Communications, 2024, vol. 15, issue 1, 1-23

Abstract: Abstract Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND’s superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues.

Date: 2024
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DOI: 10.1038/s41467-024-52445-9

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