ArticleNucleic acids research2020
scIGANs: single-cell RNA-seq imputation using generative adversarial networks.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Exploring novel kinetics of automated HMicrobiology spectrum · 2026Article
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- Cross-platform metabolomics imputation using importance-weighted autoencoders.NPJ systems biology and applications · 2026Article
- scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation.Briefings in bioinformatics · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- GSCI: A Generative and Sparse Compressed Sensing Imputation Framework for Single-Cell RNA-Sequencing Dropout Recovery.Computational and structural biotechnology journal · 2026Article
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- DeepPNCC: reconstructing pseudo-spatial cell-cell interaction landscapes from single-cell data to decipher breast cancer pathogenesis.Journal of translational medicine · 2025Article
- Review
- Artificial Intelligence Driven Innovation: Advancing Mesenchymal Stem Cell Therapies and Intelligent Biomaterials for Regenerative Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- SmartImpute is a targeted imputation framework for single-cell transcriptome data.Cell reports methods · 2025Article
12 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.
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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.