Adhikari Sikta Das, Steele Nina G, Theisen Brian, Wang Jianrong, Cui Yuehua
Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI.
Department of Statistics and Probability, Michigan State University, East Lansing, MI.
bioRxiv. 2024 Aug 25:2024.08.23.609477. doi: 10.1101/2024.08.23.609477.
Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis.
空间转录组学的最新进展显著加深了我们对生物学的理解。一个主要重点是识别空间可变基因（SVG），这对于诸如空间域检测等下游任务至关重要。传统方法通常为此使用所有或一定数量的顶级SVG。然而，在具有许多SVG的不同数据集中，这种方法可能无法确保准确的结果。相反，按表达模式对SVG进行分组并在下游分析中使用所有SVG组可以提高准确性。此外，以这种方式对SVG进行分类类似于识别细胞类型标记基因，提供有价值的生物学见解。挑战在于将SVG准确分类到相关簇中，由于缺乏关于空间基因模式的数量和谱的先验知识而变得更加复杂。为应对这一挑战，我们提出了SPACE（考虑细胞类型效应的空间可变基因聚类），这是一个通过调整由共享细胞类型引起的混杂效应，根据其空间模式对SVG进行分类的框架，以改善空间域检测。该方法不需要关于基因簇数量、空间模式或细胞类型信息的先验知识。我们全面的模拟和实际数据分析表明，SPACE是用于空间转录组学分析的一种高效且有前景的工具。
