ArticlePLoS computational biology2021
G2S3: A gene graph-based imputation method for single-cell RNA sequencing data.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
- D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.PLoS computational biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data.Briefings in bioinformatics · 2024Article
- [Imputation method for dropout in single-cell transcriptome data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43.Frontiers in aging neuroscience · 2023Article
- Regulatory network-based imputation of dropouts in single-cell RNA sequencing data.PLoS computational biology · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.
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Registered trials
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