ArticleNature communications2023
Multi-batch single-cell comparative atlas construction by deep learning disentanglement.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Advances in the characterization of in vitro-generated red blood cells: from biophysical properties to functional applications.Stem cell research & therapy · 2025Review
- Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.Science advances · 2025Article
- Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings.Frontiers in genetics · 2025Article
- Leveraging multiple labeled datasets for the automated annotation of single-cell RNA and ATAC data.Computational and structural biotechnology journal · 2025Article
- BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells.Briefings in bioinformatics · 2024Article
- Machine learning of brain-specific biomarkers from EEG.EBioMedicine · 2024Article
- Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Multi-batch single-cell comparative atlas construction by deep learning disentanglement.Nature communications · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL's capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched 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.