ArticleBriefings in bioinformatics2023
scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification.Genomics, proteomics & bioinformatics · 2026Article
- scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder.BMC bioinformatics · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.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
- Enhancing cell subpopulation discovery in cancer by integrating single-cell transcriptome and expressed variants.Fundamental research · 2026Article
- Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.BMC bioinformatics · 2025Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data.Briefings in bioinformatics · 2025Article
- MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.Briefings in bioinformatics · 2025Article
- A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering.Scientific reports · 2025Article
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 2024Article
- scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data.Briefings in bioinformatics · 2024Article
- BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation.Briefings in bioinformatics · 2024Article
- Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.Briefings in bioinformatics · 2024Article
- DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.Frontiers in genetics · 2024Article
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7 authors.
Funding
Abstract
The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the identification of cell types and the study of cellular states at a single-cell level. Despite its significant potential, scRNA-seq data analysis is plagued by the issue of missing values. Many existing imputation methods rely on simplistic data distribution assumptions while ignoring the intrinsic gene expression distribution specific to cells. This work presents a novel deep-learning model, named scMultiGAN, for scRNA-seq imputation, which utilizes multiple collaborative generative adversarial networks (GAN). Unlike traditional GAN-based imputation methods that generate missing values based on random noises, scMultiGAN employs a two-stage training process and utilizes multiple GANs to achieve cell-specific imputation. Experimental results show the efficacy of scMultiGAN in imputation accuracy, cell clustering, differential gene expression analysis and trajectory analysis, significantly outperforming existing state-of-the-art techniques. Additionally, scMultiGAN is scalable to large scRNA-seq datasets and consistently performs well across sequencing platforms. The scMultiGAN code is freely available at https://github.com/Galaxy8172/scMultiGAN.
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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.