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bioRxiv · 10.1101/2023.08.06.552154

HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic data

Yuan, X.·Ma, Y.·Gao, R.·Cui, S.·Fa, B.·Wang, Y.·Ma, S.·Wei, T.·Ma, S. S.·Yu, Z.
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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 F1 scores (average F1 score=0.903), 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 recall=0.985, average AUC=0.788) than other comparative methods without prespecifing 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.

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Yuan, X., Ma, Y., Gao, R., Cui, S., Fa, B., Wang, Y., Ma, S., Wei, T., Ma, S. S., Yu, Z.. 2023-08-07. HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic data. https://doi.org/10.1101/2023.08.06.552154

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