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scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data
Haocheng Gu1, Hao Cheng1, Anjun Ma1,2
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Motivation:
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.
Results:
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.
Availability And Implementation:
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 Information:
Supplementary data are available at Bioinformatics online.
