Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Zhuohan Yu, Yanchi Su, Yifu Lu, Yuning Yang, Fuzhou Wang, Shixiong Zhang, Yi Chang, Ka-Chun Wong () and Xiangtao Li ()
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Zhuohan Yu: Jilin University
Yanchi Su: Jilin University
Yifu Lu: Jilin University
Yuning Yang: University of Toronto
Fuzhou Wang: City University of Hong Kong
Shixiong Zhang: City University of Hong Kong
Yi Chang: Jilin University
Ka-Chun Wong: City University of Hong Kong
Xiangtao Li: Jilin University

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Date: 2023
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DOI: 10.1038/s41467-023-36134-7

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