Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
Nat Commun. 2024 Jul 7;15(1):5700. doi: 10.1038/s41467-024-49846-1.
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 scores (average 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.
识别空间变异基因（SVGs）对于理解疾病和组织结构的时空特征至关重要，这在空间转录组学研究中构成了独特的挑战。我们提出了 HEARTSVG，这是一种基于分布的、无需检验的方法，用于快速准确地识别大规模空间转录组数据中的空间变异基因。广泛的模拟表明，HEARTSVG 优于最先进的方法，具有更高的分数（平均分数=0.948）、提高的计算效率、可扩展性和减少的假阳性（FPs）。通过对来自各种空间转录组学技术的 12 个真实数据集的分析，HEARTSVG 比其他比较方法识别出更多具有生物学意义的 SVGs（平均 AUC=0.792），而无需预先指定空间模式。此外，通过对 SVGs 进行聚类，我们在人类结直肠癌数据中发现了两个具有独特空间表达模式、时空位置和生物学功能的不同肿瘤空间域，揭示了肿瘤的复杂性。
