Abstract

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.

Details

Title
Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome
Author
Raredon Micha Sam Brickman 1 ; Yang, Junchen 2 ; Garritano, James 3 ; Wang, Meng 2 ; Kushnir, Dan 4 ; Schupp, Jonas Christian 5 ; Adams, Taylor S 5 ; Greaney, Allison M 6 ; Leiby, Katherine L 1 ; Kaminski Naftali 5 ; Kluger Yuval 7 ; Levchenko, Andre 8 ; Niklason, Laura E 9 

 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Medicine, Medical Scientist Training Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale University, Interdepartmental Program in Computational Biology and Bioinformatics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale School of Medicine, Medical Scientist Training Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Applied Mathematics Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 NOKIA Bell-Laboratories, Murray Hill, USA (GRID:grid.469490.6) (ISNI:0000 0004 0520 1282) 
 Yale School of Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale University, Applied Mathematics Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Department of Pathology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Interdepartmental Program in Computational Biology and Bioinformatics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Yale Systems Biology Institute, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale University, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Medicine, Department of Anesthesiology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2637673315
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.