Community: Component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets.

Changes in cell-cell communication during disease can result from shifts in tissue composition, variations in the proportion of cells engaged in signaling, and differences in ligand-receptor expression. To address this, we developed community, an R package designed for differential communication analysis in multi-sample case-control scRNAseq datasets. Community reconstructs interactions by evaluating cell type abundance, the active fraction of cells, and their expression levels. This method identifies communication patterns that are upregulated, downregulated, unchanged, or compensated. Applied to ulcerative colitis, melanoma under immune checkpoint inhibitor treatment, and acute myeloid leukemia, community captured disease- and response-associated communication changes, including increased communication in ulcerative colitis, reduced immunosuppressive signaling in melanoma responders, and decreased immune communication in AML. Comparisons with existing tools showed improved robustness to outlier-driven signals and better scalability. These component-level analyses help connect altered communication patterns to biological mechanisms in healthy and disease states.