ArticleNature machine intelligence2021
Simultaneous deep generative modeling and clustering of single cell genomic data.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.Nature communications · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- scMGCL: accurate and efficient integration representation of single-cell multi-omics data.Bioinformatics (Oxford, England) · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation.Nature communications · 2025Article
- MetaQ: fast, scalable and accurate metacell inference via single-cell quantization.Nature communications · 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
- Unlocking cross-modal interplay of single-cell joint profiling with CellMATE.Briefings in bioinformatics · 2024Article
- Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2024Article
- An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.Briefings in bioinformatics · 2024Article
- ctGAN: combined transformation of gene expression and survival data with generative adversarial network.Briefings in bioinformatics · 2024Article
- scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders.Nature communications · 2024Article
- DANCE: a deep learning library and benchmark platform for single-cell analysis.Genome biology · 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
- scVIC: deep generative modeling of heterogeneity for scRNA-seq data.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we proposed scDEC, a computational tool for single cell ATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks (GANs), and is capable of learning the latent representation and inferring the cell labels, simultaneously. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In downstream applications, we demonstrated that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations. We also showed that it is possible to extend scDEC for the integrative analysis of multi-modal single cell data.
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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.