Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Comput Biol Med. 2024 Mar;171:108211. doi: 10.1016/j.compbiomed.2024.108211. Epub 2024 Feb 28.
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics. scRNA-seq analysis represents a challenging and cutting-edge frontier within the field of biological research. Differential geometry serves as a powerful mathematical tool in various applications of scientific research. In this study, we introduce, for the first time, a multiscale differential geometry (MDG) strategy for addressing the challenges encountered in scRNA-seq data analysis. We assume that intrinsic properties of cells lie on a family of low-dimensional manifolds embedded in the high-dimensional space of scRNA-seq data. Multiscale cell-cell interactive manifolds are constructed to reveal complex relationships in the cell-cell network, where curvature-based features for cells can decipher the intricate structural and biological information. We showcase the utility of our novel approach by demonstrating its effectiveness in classifying cell types. This innovative application of differential geometry in scRNA-seq analysis opens new avenues for understanding the intricacies of biological networks and holds great potential for network analysis in other fields.
单细胞 RNA 测序 (scRNA-seq) 已经成为一种变革性的技术，为深入了解细胞多样性和基因表达动态的复杂格局提供了无与伦比的视角。scRNA-seq 分析代表了生物学研究领域极具挑战性和前沿性的领域。微分几何在科学研究的各种应用中是一种强大的数学工具。在这项研究中，我们首次引入了一种多尺度微分几何 (MDG) 策略，以解决 scRNA-seq 数据分析中遇到的挑战。我们假设细胞的内在特性位于嵌入 scRNA-seq 数据高维空间的低维流形族上。构建多尺度细胞-细胞交互流形以揭示细胞-细胞网络中的复杂关系，其中基于曲率的细胞特征可以解析复杂的结构和生物学信息。我们通过展示其在细胞类型分类中的有效性来展示我们新方法的实用性。微分几何在 scRNA-seq 分析中的这一创新应用为理解生物网络的复杂性开辟了新途径，并为其他领域的网络分析提供了巨大潜力。
