ArticleNature communications2021
RA3 is a reference-guided approach for epigenetic characterization of single cells.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.Interdisciplinary sciences, computational life sciences · 2026Article
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering.Interdisciplinary sciences, computational life sciences · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.Briefings in bioinformatics · 2025Article
- CASHeart: A database of single cells chromatin accessibility for the human heart.Quantitative biology (Beijing, China) · 2025Article
- INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.Nature communications · 2025Article
- Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic.Environmental health perspectives · 2025Article
- Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.Genome biology · 2024Article
- Omics-Driven Strategies for Developing Saline-Smart Lentils: A Comprehensive Review.International journal of molecular sciences · 2024Review
- scATAcat: cell-type annotation for scATAC-seq data.NAR genomics and bioinformatics · 2024Article
- DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-cell Transcriptomics Data.Genomics, proteomics & bioinformatics · 2024Article
- scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders.Nature communications · 2024Article
- EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2024Article
- Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance.Quantitative biology (Beijing, China) · 2024Article
- scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data.Nature communications · 2024Article
- PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.Nature communications · 2024Article
- OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regions.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data. Here, we present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Accessibility data), which utilizes the information in massive existing bulk chromatin accessibility and annotated scCAS data. RA3 simultaneously models (1) the shared biological variation among scCAS data and the reference data, and (2) the unique biological variation in scCAS data that identifies distinct subpopulations. We show that RA3 achieves superior performance when used on several scCAS datasets, and on references constructed using various approaches. Altogether, these analyses demonstrate the wide applicability of RA3 in analyzing scCAS data.
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