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

• Published in: Nature communications Vol. 15; no. 1; p. 600
• Main Authors: Liang, Yuchen, Shi, Guowei, Cai, Runlin, Yuan, Yuchen, Xie, Ziying, Yu, Long, Huang, Yingjian, Shi, Qian, Wang, Lizhe, Li, Jun, Tang, Zhonghui
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18-01-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 38/39; 631/114/1305; 631/114/2397; 631/114/2401; 631/114/794; 631/61/514/1949; Cluster Analysis; Clustering; Gene expression; Gene Expression Profiling; Genes; Genetic diversity; Humanities and Social Sciences; multidisciplinary; Recognition, Psychology; Science; Science (multidisciplinary); Spatial variations; Transcriptome - genetics; Transcriptomics; Zygote Intrafallopian Transfer
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-44835-w

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.
Understanding biological mechanisms requires a thorough exploration of spatiotemporal transcriptional patterns in complex tissues. Here, authors present PROST to quantify spatial gene expression patterns and detect spatial domains using spatial transcriptomics data of varying resolutions.