LRLoop

LRLoop analyzes ligand-receptor (LR) loops to identify bi-directional feedback interactions between cell types from bulk and single-cell RNA sequencing data.

Key Features:

• Bi-Directional Interaction Analysis: Identifies pairs of ligand-receptor interactions that are mutually responsive and form closed feedback loops between cell types.
• Reduction in False Positives: Assesses interactions on bulk datasets to reduce false positive rates compared with traditional LR methods.
• Single-Cell Data Strategy: Evaluates single-cell performance by using between-tissue interactions as an indicator of potential false positives and reports a lower fraction of such interactions than traditional methods.
• Application in Developmental Biology (retinal development): Applied to single-cell retinal development datasets to uncover bi-directional LR interactions linked to proliferation, neurogenesis, and cell fate specification.

Scientific Applications:

• Developmental biology: Identification of feedback loops underlying processes such as proliferation, neurogenesis, and cell fate specification, including retinal development studies.
• Tissue regeneration: Analysis of reciprocal LR signaling that may regulate repair and regenerative processes.
• Cancer progression: Characterization of intercellular feedback motifs that can influence tumor progression and tumor–microenvironment interactions.
• Intercellular signaling network analysis: Reconstruction of intricate signaling networks by detecting closed feedback motifs among cell types.

Methodology:

Implemented as an R package that analyzes bulk and single-cell RNA-seq gene expression datasets to detect mutually responsive ligand-receptor pairs forming closed feedback loops, uses between-tissue interactions to estimate false positives in single-cell data, and validates performance by comparison with traditional LR methods on bulk and single-cell datasets.