Computation and visualization of cell-cell signaling topologies in single-cell systems data using Connectome

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular communication.
• Quantifying and visualizing cell-cell connectivity in scRNA-seq data presents significant computational challenges.
• Understanding ligand-receptor interactions is crucial for tissue and organ function.

Purpose of the Study:

• To introduce Connectome, an R software package for analyzing cell-cell signaling networks.
• To facilitate rapid calculation and interactive exploration of signaling network topologies from scRNA-seq data.
• To enable differential and comparative connectomics across diverse tissue systems.

Main Methods:

• Development of the Connectome R package.
• Utilizing reference sets of known ligand-receptor mechanisms.
• Implementation of computational and graphical tools for network analysis and visualization.

Main Results:

• Connectome allows for efficient computation of cell-cell signaling network topologies.
• The package supports interactive exploration of these networks.
• It enables comparative analysis of signaling networks between different tissue types.

Conclusions:

• Connectome addresses key computational challenges in analyzing scRNA-seq data.
• It provides tools to reveal biological insights from cell-cell connectivity patterns.
• The software facilitates a deeper understanding of tissue biology through network analysis.