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

Abstract

Cell-cell communication is essential for physiological tissue function. In disease, communication often gets disbalanced by changes in the tissue cell type composition, fraction of cells engaged in communication, and changes in expression levels of ligands, receptors and adhesion molecules. Single cell RNAseq analyses allow to measure these parameters in healthy and diseased tissues. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq data between large cohorts of cases and controls. Community performs differential analysis to identify communication channels affected in disease by reconstructing the communication between different cell types using three components: cell type abundance, fraction of active cells, and ligand/receptor expression levels, both in cases and controls. This approach allows to not only identify up- or down-regulated interactions, but also detect cases of compensation, where a shift in one component gets compensated by a counter-shift in another component, keeping the levels of communication stable. The component analysis enables us to better understand the underlying biological processes leading to changes in communication. We demonstrate the performance of community by using two disease entities, ulcerative colitis and acute myeloid leukemia. We compared the performance of our tool to other differential communication pipelines, which community outperformed in robust identification of up- and down-regulated interactions, as well as its unique feature of identifying compensated communication shifts. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets, and disentangle the driving mechanisms of communication shifts in disease.