CITEdb: a manually curated database of cell–cell interactions in human

Abstract

Abstract Motivation The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell–cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell–cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell–cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell–cell interactions at the cell type level, which hinders the development of computational approaches to identify cell–cell interactions. Results We develop a publicly accessible database, CITEdb (Cell–cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell–cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell–cell interactions from different tools. CITEdb contains 728 pairs of cell–cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand–receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell–cell interactions. Users can search for cell–cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell–cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell–cell interactions. Availability and implementation CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. Supplementary information Supplementary data are available at Bioinformatics online.