Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data

Raredon, M. S. B.; Yang, J.; Garritano, J.; Wang, M.; Kushnir, D.; Schupp, J. C.; Adams, T. S.; Greaney, A. M.; Leiby, K. L.; Kaminski, N.; Kluger, Y.; Levchenko, A.; Niklason, L. E.

doi:10.1101/2021.01.21.427529

bioRxiv · 10.1101/2021.01.21.427529

Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data

Raredon, M. S. B.·Yang, J.·Garritano, J.·Wang, M.·Kushnir, D.·Schupp, J. C.·Adams, T. S.·Greaney, A. M.·Leiby, K. L.·Kaminski, N.·Kluger, Y.·Levchenko, A.·Niklason, L. E.

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Abstract

Single-cell RNA-sequencing data can revolutionize our understanding 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 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 complete mechanistic networks are quantitatively compared between systems. Connectome includes computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets. We present approaches to quantify these topologies and discuss some of the biologic theory leading to their design.

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Raredon, M. S. B., Yang, J., Garritano, J., Wang, M., Kushnir, D., Schupp, J. C., Adams, T. S., Greaney, A. M., Leiby, K. L., Kaminski, N., Kluger, Y., Levchenko, A., Niklason, L. E.. 2021-01-21. Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data. https://doi.org/10.1101/2021.01.21.427529

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