MultiNicheNet

MultiNicheNet analyzes differential cell–cell communication across multiple samples and conditions using multi-sample single-cell RNA sequencing (scRNA-seq) data to identify differentially expressed and active ligand–receptor pairs and their downstream target genes.

Key Features:

• Differential Expression Analysis: Leverages state-of-the-art differential expression algorithms tailored for multi-sample scRNA-seq to infer differentially expressed and active ligand–receptor pairs between conditions.
• Integration with NicheNet-v2: Incorporates NicheNet-v2 with a refined ligand–receptor network and an improved ligand–target prior knowledge model to enhance prediction of downstream target genes.
• Handling Complex Experimental Designs: Corrects for batch effects and covariates to manage multifactorial experimental designs and inter-sample heterogeneity.
• Application to Disease Cohorts: Applied to patient cohort datasets from breast cancer, squamous cell carcinoma, multisystem inflammatory syndrome in children, lung fibrosis, and idiopathic pulmonary fibrosis to identify known and novel aberrant signaling processes.
• Therapeutic Response Analysis: Enables analysis of changes in cell–cell communication in response to therapy, including between- and within-group differences in signaling dynamics.

Scientific Applications:

• Disease-focused communication analysis: Dissects intercellular communication alterations associated with disease phenotypes using multi-sample scRNA-seq data.
• Biomarker and target discovery: Supports identification of candidate biomarkers and therapeutic targets by linking ligand–receptor interactions to downstream target genes.
• Large-scale and atlas integration: Suited for large-scale studies and integrated atlas data where correction for batch effects and complex designs is required.

Methodology:

Uses differential expression algorithms for multi-sample scRNA-seq, integrates the NicheNet-v2 ligand–receptor network and ligand–target prior, corrects for batch effects and covariates, and infers active ligand–receptor pairs with predicted downstream target genes.