ArticleNature methods2024
A fast, scalable and versatile tool for analysis of single-cell omics data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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Who cites it
133 citing papers in PubMed, 139 citations in OpenAlex.
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- Single-Nucleus Multi-Omic Atlas Maps Regulatory Architecture and Non-Coding Variant Effects across Adult Human Tissues.bioRxiv : the preprint server for biology · 2026Article
- A temporal single cell atlas of dynamic chromatin accessibility during murine lung maturation.Nature communications · 2026Article
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- Alignment-free integration of single-nucleus ATAC-seq across species with sPYce.Nature ecology & evolution · 2026Article
- Ragomics-Meta: An open-source, scalable, reproducible, and extensible multi-omics analysis workflow.iMetaOmics · 2026Article
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- Non-canonical NF-κB drives a fate switch from germinal center to early effector B cells.iScience · 2026Article
- APOE genotypes differentially remodel the astrocytic lipid droplet proteome to shape lipid droplet dynamics.Nature communications · 2026Article
- Simultaneous single-cell profiling of chromatin, transcriptome and surface markers with OneCell CUT&Tag captures epigenomic reprogramming.Nature biotechnology · 2026Article
- Positional grammar of transcription factor binding partitions developmental and stress-response regulation in plants.Nature communications · 2026Article
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.Nature communications · 2026Article
- ATACompass enables cross-species cell identity mapping for scATAC-seq without gene annotations.Cell reports methods · 2026Article
- SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.iScience · 2026Article
- Post-vaccination expansion of extrafollicular Th10 and regulatory Tfr cells distinguishes strong from weak influenza vaccine responses in older adults.medRxiv : the preprint server for health sciences · 2026Article
73 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.
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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.