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

作者：Yu, Zhuohan; Su, Yanchi; Lu, Yifu; Yang, Yuning; Wang, Fuzhou; Zhang, Shixiong; Chang, Yi; Wong, Ka-Chun^*; Li, Xiangtao^*

来源：Nature Communications, 2023, 14(1): 400.

DOI：10.1038/s41467-023-36134-7

• RNA-SEQ DATA
• EXPRESSION
• PROLIFERATION
• SIGNATURES
• ATLAS

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. @@@ A major challenge in analyzing scRNA-seq data arises from challenges related to dimensionality and the prevalence of dropout events. Here the authors develop a deep graph learning method called scMGCA based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments, outperforming other state-of-the-art models across multiple platforms.

• 单位
  吉林大学