Scientific ReportsSystems BiologyPeer-reviewed journal article
Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data
Micha Sam Brickman RaredoniD, Junchen YangiD, James GarritanoiD, Meng WangiD, Dan KushniriD, Jonas C. SchuppiD, Taylor Sterling AdamsiD, Allison Marie GreaneyiD, Katherine L. LeibyiD, Naftali KaminskiiD, Yuval KlugeriD, Andre LevchenkoiD, Laura E. NiklasoniD
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.
- assay: scRNA-seq
- research focus: cell-cell communication
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 139
- FWCI
- 9.06
- Field percentile
- top 1%
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