ArticleBioinformatics (Oxford, England)2022
scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- A Novel Dual-Level Momentum Distillation Method with Extreme Thresholding for Imputing Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2026Article
- scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- SLECA: A Single-Cell Atlas of Systemic Lupus Erythematosus Enabling Rare-Cell Discovery Using Graph Transformer.Computational and structural biotechnology journal · 2026Article
- AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2025Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- scQA: A dual-perspective cell type identification model for single cell transcriptome data.Computational and structural biotechnology journal · 2024Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data.Biology · 2024Article
- Representing and extracting knowledge from single-cell data.Biophysical reviews · 2024Review
- MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.Nature communications · 2024Article
- scVIC: deep generative modeling of heterogeneity for scRNA-seq data.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
motivationGene 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.
resultsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.