Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Medical Scientist Training Program, Yale School of Medicine, New Haven, CT, USA.
Sci Rep. 2022 Mar 9;12(1):4187. doi: 10.1038/s41598-022-07959-x.
Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.
单细胞 RNA 测序数据极大地提高了我们理解影响组织和器官功能的细胞间和配体-受体连接模式的能力。然而，以一种能够为组织生物学提供信息的方式对这些模式进行量化和可视化是主要的计算和认识论挑战。在这里，我们提出了 Connectome，这是一个用于 R 的软件包，它可以方便地计算和交互式探索单细胞 RNA 测序数据中包含的细胞间信号网络拓扑结构。Connectome 可以与任何已知的配体-受体机制的参考集一起使用。它具有内置功能，可促进差异和比较连接组学，即在组织系统之间比较信号网络。Connectome 专注于分析和探索跨不同单细胞数据集的细胞间连接模式以及揭示生物学见解的计算和图形工具。我们提出了定量有针对性的网络拓扑结构的方法，并讨论了导致其设计的一些生物学理论。
