HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Xin Yuan, Yanran Ma, Ruitian Gao, Shuya Cui, Yifan Wang, Botao Fa, Shiyang Ma, Ting Wei, Shuangge Ma () and Zhangsheng Yu ()
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Xin Yuan: Shanghai Jiao Tong University
Yanran Ma: Shanghai Jiao Tong University
Ruitian Gao: Shanghai Jiao Tong University
Shuya Cui: Shanghai Jiao Tong University
Yifan Wang: Shanghai Jiao Tong University
Botao Fa: Xi’an Jiaotong University
Shiyang Ma: Shanghai Jiao Tong University School of Medicine
Ting Wei: Shanghai Jiao Tong University
Shuangge Ma: Shanghai Jiao Tong University
Zhangsheng Yu: Shanghai Jiao Tong University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher $${F}_{1}$$ F 1 scores (average $${F}_{1}$$ F 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Date: 2024
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DOI: 10.1038/s41467-024-49846-1

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