PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics

Yuchen Liang, Guowei Shi, Runlin Cai, Yuchen Yuan, Ziying Xie, Long Yu, Yingjian Huang, Qian Shi, Lizhe Wang, Jun Li () and Zhonghui Tang ()
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Yuchen Liang: Sun Yat-sen University
Guowei Shi: Sun Yat-sen University
Runlin Cai: Sun Yat-sen University
Yuchen Yuan: Sun Yat-sen University
Ziying Xie: Sun Yat-sen University
Long Yu: Sun Yat-sen University
Yingjian Huang: Sun Yat-sen University
Qian Shi: Sun Yat-sen University
Lizhe Wang: China University of Geosciences
Jun Li: China University of Geosciences
Zhonghui Tang: Sun Yat-sen University

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: Abstract Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.

Date: 2024
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DOI: 10.1038/s41467-024-44835-w

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