SingleCellSignalR

SingleCellSignalR infers intercellular ligand–receptor interaction networks from single-cell RNA sequencing (scRNA-seq) data to map cellular communication and relate receptors to expressed intracellular pathways.

Key Features:

• Curated Ligand–Receptor Database: Utilizes a curated database of ligand–receptor pairs as the basis for interaction inference.
• Novel Regularized Scoring Scheme: Applies a regularized scoring method to assess confidence in predicted ligand–receptor interactions and control false positives.
• R Package Implementation: Implemented as an R package for computational analysis.
• Diverse Output Formats: Produces tabular and graphical outputs, including comprehensive network views that integrate intercellular interactions.
• Pathway Integration Functionality: Relates receptors to expressed intracellular pathways to explore downstream signaling effects.
• Comparative Analysis with Related Tools: Includes comparisons with other related tools to contextualize and benchmark predictions.

Scientific Applications:

• Tissue Microenvironment Analysis: Identifies ligand–receptor interactions that underlie cellular communication in complex tissue environments.
• Mouse Epidermis Communication Mapping: Revealed an oriented communication structure from external to basal layers in mouse epidermis scRNA-seq data.

Methodology:

Infers interactions from scRNA-seq expression using a curated ligand–receptor database, applies a regularized scoring scheme to score interactions, maps receptors to expressed intracellular pathways, and outputs tabular and graphical network representations while enabling comparative analyses with other tools.