scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data

• Published in: Bioinformatics (Oxford, England) England), 2022-11, Vol.38 (23), p.5322-5325
• Main Authors: Gu, Haocheng, Cheng, Hao, Ma, Anjun, Li, Yang, Wang, Juexin, Xu, Dong, Ma, Qin
• Format: Article
• Language: English
• Subjects: Applications Note; Cluster Analysis; Gene Expression Profiling - methods; Neural Networks, Computer; RNA-Seq; Sequence Analysis, RNA - methods; Single-Cell Analysis; Software
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btac684

Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. Supplementary data are available at Bioinformatics online.