LRLoop: a method to predict feedback loops in cell–cell communication

Abstract Motivation Intercellular communication (i.e. cell–cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell–cell communication based on gene expression datasets, these methods often predict one-directional ligand–receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. Results Here, we describe ligand–receptor loop (LRLoop), a new method for analyzing cell–cell communication based on bi-directional ligand–receptor interactions, where two pairs of ligand–receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand–receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification. Availability and implementation An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1). Supplementary information Supplementary data are available at Bioinformatics online.