ArticleBriefings in bioinformatics2021
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 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
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- A unified framework for correcting batch effects and integrating multi-omics data.Scientific reports · 2026Article
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- scII: Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation.Journal of chemical information and modeling · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Leveraging Hamiltonian neural flow for robust single-cell multi-omics integration: application to Alzheimer's disease.Frontiers in genetics · 2026Article
- Vector-guided graph learning for spatial multi-slice multi-omics alignment.Cell reports methods · 2025Article
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics.bioRxiv : the preprint server for biology · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs.Nature communications · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- Nonlinear embedding and integration of omics data: a fast and tuning-free approach.Briefings in bioinformatics · 2025Article
- FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.Briefings in bioinformatics · 2025Article
- Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent sparse and heterogeneous data are lacking. Here, we present a single-cell multimodal variational autoencoder model, which combines three types of joint-learning strategies with a probabilistic Gaussian Mixture Model to learn the joint latent features that accurately represent these multilayer profiles. Studies on both simulated datasets and real datasets demonstrate that it has more preferable capability (i) dissecting cellular heterogeneity in the joint-learning space, (ii) denoising and imputing data and (iii) constructing the association between multilayer omics data, which can be used for understanding transcriptional regulatory mechanisms.
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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.