ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for cell type annotation. Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. scTAG integrates the zero-inflated negative binomial (ZINB) model into a topology adaptive graph convolutional autoencoder to learn the lowdimensional latent representation and adopts Kullback–Leibler (KL) divergence for the clustering tasks. By simultaneously optimizing the clustering loss, ZINB loss, and the cell graph reconstruction loss, scTAG jointly optimizes cluster label assignment and feature learning with the topological structures preserved in an end-to-end manner. Extensive experiments on 16 single-cell RNA-seq datasets from diverse yet representative single-cell sequencing platforms demonstrate the superiority of scTAG over various state-of-the-art clustering methods.

Authors

• Zhuohan Yu Jilin University
• Yifu Lu
• Yunhe Wang Huawei Noah's Ark Lab
• Fan Tang Institute of Computing Technology, Chinese Academy of Sciences
• Ka-Chun Wong Department of Computer Science, City University of Hong Kong Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong SAR
• Xiangtao Li School of Artificial Intelligence, Jilin University

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