ArticleCell reports methods2022
PeakVI: A deep generative model for single-cell chromatin accessibility analysis.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- Article
- Flexible use of conserved motifs constrains genome access in cell type evolution.Nature ecology & evolution · 2026Article
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Article
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.Bioinformatics (Oxford, England) · 2026Article
- Cross-Modal Denoising and Integration of Spatial Multi-Omics Data with CANDIES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Multimodal atlas of human atherosclerosis links granular vascular cell states to coronary artery disease risk.medRxiv : the preprint server for health sciences · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.Genome biology · 2026Article
- A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.Nature communications · 2026Article
- scMultiPreDICT: A single-cell predictive framework with transcriptomic and epigenetic signatures.bioRxiv : the preprint server for biology · 2026Article
- SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiation.bioRxiv : the preprint server for biology · 2026Article
- A multi-omic single-cell landscape of perinatal mouse skin maps lineage specification and reveals shared dynamics in human fetal skin.Experimental & molecular medicine · 2026Article
- Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.Genome biology · 2026Article
- Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex.Nature communications · 2026Article
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Benchmarking LLM-based agents for single-cell omics analysis.Genome biology · 2026Article
- Human-scATAC-Corpus: a comprehensive database of scATAC-seq data.Nucleic acids research · 2026Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here, we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects, such as library size and region-specific biases. In addition, PeakVI provides a technique for identifying differential accessibility at a single-region resolution, which can be used for cell-type annotation as well as identification of key
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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.