ArticleProceedings of the National Academy of Sciences of the United States of America2023
An empirical Bayes method for differential expression analysis of single cells with deep generative models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- ResolVI: addressing noise and bias in spatial transcriptomics.Nature methods · 2026Article
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
- Integrated skin-lesion transcriptomics prioritises biomarker-candidate host-response genes with pharmacological annotation in leprosy type 1 reaction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data.bioRxiv : the preprint server for biology · 2026Article
- High-Dimensional Single-Cell Analysis Reveals Coordinated Age-Dependent Neuroinflammatory Microglia-T cell Circuits in the Brain.bioRxiv : the preprint server for biology · 2025Article
- Inferring differential dynamics from multi-lineage, multi-omic, and multi-sample single-cell data with MultiVeloVAE.Nature communications · 2025Article
- scSpecies: enhancement of network architecture alignment in comparative single-cell studies.Genome biology · 2025Article
- Pathological role of immunoproteasome PSMB8 in Parkinson's disease: a link between α-synuclein aggregation and immune activation.EBioMedicine · 2025Article
- Deep generative modeling of sample-level heterogeneity in single-cell genomics.Nature methods · 2025Article
- Scvi-hub: an actionable repository for model-driven single-cell analysis.Nature methods · 2025Article
- Cross-species imputation and comparison of single-cell transcriptomic profiles.Genome biology · 2025Article
- Article
- Reduced circulating sphingolipids andScience advances · 2025Article
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review.International journal of stem cells · 2024Review
- VI-VS: calibrated identification of feature dependencies in single-cell multiomics.Genome biology · 2024Article
- Article
- Cross-species imputation and comparison of single-cell transcriptomic profiles.bioRxiv : the preprint server for biology · 2024Article
- Dependency-aware deep generative models for multitasking analysis of spatial omics data.Nature methods · 2024Article
- Bayesian estimation of gene constraint from an evolutionary model with gene features.Nature genetics · 2024Article
Corrections and comments
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6 authors.
Funding
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Abstract
Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been paid to the problem of utilizing the uncertainty from the deep generative model for differential expression (DE). Furthermore, the existing approaches do not allow for controlling for effect size or the false discovery rate (FDR). Here, we present lvm-DE, a generic Bayesian approach for performing DE predictions from a fitted deep generative model, while controlling the FDR. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform state-of-the-art methods at estimating the log fold change in gene expression levels as well as detecting differentially expressed genes between subpopulations of cells.
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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.