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BioinformaticsbioRxiv
Preprint

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

Wenyi YangiD, Zhaochun XuiD, Meng LuoiD, Yideng CaiiD, Chang XuiD, Pingping WangiD, Songren WeiiD, Guangfu XueiD, Xiyun JiniD, Rui ChengiD, Jinhao QueiD, Wenyang ZhouiD, Fenglan PangiD, Huan NieiD, Qinghua JiangiD

PDFDOI

Abstract

With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (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. To address the issue, 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.

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