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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

  • Zhuohan Yu
  • Yanchi Su
  • Yifu Lu
  • Yuning Yang
  • Yi Chang
  • Xiangtao Li

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Detail(s)

Original languageEnglish
Article number400
Journal / PublicationNature Communications
Volume14
Online published25 Jan 2023
Publication statusPublished - 2023

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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. © 2023, The Author(s).

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