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| Home > Publications database > Community: Component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets |
| Home > Publications database > Community: Component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets |
| Journal Article | DKFZ-2026-02181 |
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2026
Elsevier
St. Louis
Abstract: Changes in cell-cell communication during disease can result from shifts in tissue composition, variations inthe 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-samplecase-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 thatare upregulated, downregulated, unchanged, or compensated. Applied to ulcerative colitis, melanoma underimmune checkpoint inhibitor treatment, and acute myeloid leukemia, community captured disease- andresponse-associated communication changes, including increased communication in ulcerative colitis,reduced immunosuppressive signaling in melanoma responders, and decreased immune communicationin AML. Comparisons with existing tools showed improved robustness to outlier-driven signals and betterscalability. These component-level analyses help connect altered communication patterns to biologicalmechanisms in healthy and disease states.
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