scLR

scLR identifies and analyzes dysregulated ligand-receptor interactions between conditions using single-cell transcriptomic data to characterize changes in cell-cell communication.

Key Features:

• Simultaneous consideration of ligands and receptors: Considers ligand and receptor expression jointly to identify dysregulated interactions across multiple conditions.
• Statistical modeling of expression product: Models the distribution of the product of ligand and receptor expression values to assess interaction changes.
• Accounting for inter-sample variance and small sample sizes: The modeling approach explicitly accounts for inter-sample variances and limited sample sizes common in single-cell studies.
• Validation by simulation studies: Simulation studies demonstrate high sensitivity and specificity for detecting significant changes in interactions.

Scientific Applications:

• Study of cellular communication in biological processes: Applicable to investigations of differentiation, development, homeostasis, and inflammation.
• Cytokine signaling in COVID-19: Used to uncover cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection.
• TGF-β signaling in pulmonary fibrosis: Identified activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis.

Methodology:

Models the distribution of the product of ligand and receptor expressions and incorporates inter-sample variance and small sample size considerations to detect dysregulated interactions across conditions.