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

• DOI: 10.1093/bioinformatics/btad596
• Publication Date: 2023-09-23T20:11:16Z
• Authors: Wenyi Yang; Pingping Wang; Meng Luo; Yideng Cai; Chang Xu; Guangfu Xue; Xiyun Jin; Rui Cheng; Jinhao Que; Fenglan Pang; Yuexin Yang; Huan Nie; Qinghua Jiang; Zhigang Liu; Zhaochun Xu
• Keywords: 0301 basic medicine; Original Paper; 03 medical and health sciences; Deep Learning; Sequence Analysis, RNA; Gene Expression Profiling; Cluster Analysis; Single-Cell Analysis; Software

Abstract

Motivation 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.

Results 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.

Availability and implementation The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.