STACCato: Identifying condition-related cell-cell communication events using supervised tensor analysis

Abstract

Numerous tools have been developed to infer active cell-cell communication (CCC) events, which are essential for understanding biological processes and diseases. However, existing downstream methods for assessing the relationships between CCC events and biological conditions lack clear interpretation, fail to adjust for confounders, and ignore dependencies among CCC events. To address these limitations, we introduce STACCato, a Supervised Tensor Analysis tool designed to identify Condition-related Cell-cell communication events. STACCato employs a tensor-based regression model to enable statistical inference related to the relationships between biological conditions (e.g., disease status, tissue types) and specific CCC events, while adjusting for confounders and CCC dependencies. Through extensive simulations and real-world applications on scRNA-seq datasets of lupus and autism, we demonstrate that STACCato consistently provides improved inference of condition-related CCC events compared to alternative methods. The computational tool implementing the STACCato framework is available on GitHub.