DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

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Abstract

Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.