Journal Article

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

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

2023

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114 / 631/114/2397 / 631/1647/48 / 631/1647/514/1949 / 639/705/1046 / Adenocarcinoma / Annotations / Carcinoma, Pancreatic Ductal - genetics / Clusters / Data analysis / Embedding / Gene expression / Gene Expression Profiling - methods / Gene Expression Regulation / Gene regulation / Gene sequencing / Heterogeneity / Humanities and Social Sciences / Humans / Learning / multidisciplinary / Pancreatic cancer / Pancreatic Neoplasms - genetics / Representations / Science / Science (multidisciplinary) / Signal transduction / Single-Cell Analysis - methods / Teaching methods / Topology / Transcriptome / Transcriptomics