DiSiR

DiSiR identifies and quantifies ligand-receptor interactions at the subunit level from single-cell RNA-sequencing (scRNA-seq) data to analyze cell-cell signaling pathways.

Key Features:

• Permutation-Based Framework: Employs a permutation-based statistical approach to infer cell-cell interactions from scRNA-seq data.
• Subunit-Level Analysis: Resolves interactions involving multi-subunit receptors by analyzing expression at the receptor-subunit level.
• Pathway-Specific Queries: Supports targeted investigation of user-defined signaling pathways.
• Database Flexibility: Can analyze curated ligand-receptor databases and infer interactions not listed in existing resources.
• Performance Superiority: Demonstrated superior performance relative to permutation-based methods such as CellPhoneDB and ICELLNET on simulated and real datasets.

Scientific Applications:

• Disease Mechanism Studies: Applied to scRNA-seq datasets such as COVID lung and rheumatoid arthritis (RA) synovium samples to compare inflammatory signaling between control and diseased states.
• Pathway-Specific Research: Enables hypothesis generation and testing for specific signaling mechanisms via targeted pathway analysis.

Methodology:

Analyzes scRNA-seq expression data to map interactions involving multi-subunit ligand-activated receptors using a permutation-based framework for statistical validation and accommodates both known and novel ligand-receptor pairs.