talklr

talklr infers ligand–receptor-mediated intercellular communication from single-cell RNA-seq (scRNA-seq) data using information-theoretic measures, including Kullback–Leibler divergence, to detect interaction changes across cell types and conditions.

Key Features:

• Holistic Analysis: Integrates ligand and receptor expression across multiple cell types to assess potential communication pathways.
• Information Theory-Based Approach: Applies Kullback–Leibler divergence to identify statistically significant ligand–receptor interactions.
• Performance Superiority: Demonstrated superior performance in identifying ligand–receptor interactions relevant to tissue-specific functions and disease mechanisms across datasets.
• Unbiased Signaling Event Revelation: Detects signaling events by considering joint ligand–receptor patterns rather than analyzing gene expression independently per cell type.

Scientific Applications:

• Tissue Homeostasis: Identifies key ligand–receptor interactions that contribute to tissue maintenance.
• Disease Mechanisms: Detects perturbations in intercellular communication pathways associated with disease and highlights potential targets for therapeutic intervention.

Methodology:

Uses information-theoretic measures, specifically Kullback–Leibler divergence, to assess expression changes of ligands and receptors jointly across multiple cell types and conditions, contrasting with per-cell-type independent analyses.