Deep generative modeling for single-cell transcriptomics
Open accessPublished version of a preprint
Abstract
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.
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Both Harmony's and scVI's design inherently incorporates batch labels in its training process.
“Importantly, both Harmony’s and scVI’s design inherently incorporates batch labels in its training process.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [20]
The Discussion (page not provided) says scVI explicitly accounts for batch annotations, and the Online Methods describe batch-dependent parameters in its model. Our copy does not address Harmony’s design or training, so it supports only the scVI part of the sentence. (LLM, conf=0.89)
Experimental — accuracy not yet measuredBoth scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space, and then these cell embeddings are fine-tuned for cell type classification.
“Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space [18–21], and then these cell embeddings are fine-tuned for cell type classification.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [20]
The Results section (‘The scVI model: definition and preliminary evaluation’; page unavailable) describes representing noisy single-cell expression as a low-dimensional latent vector intended to better reflect biological differences between cells. That supports the general rationale for cell embeddings, but the paper describes scVI, not scGPT or Geneformer, so it does not establish that those named models produce embeddings for this purpose. The fine-tuning clause follows the citation and is not assessed as attributed to this source. (LLM, conf=0.87)
Experimental — accuracy not yet measuredZero-inflated autoencoders were introduced to handle data sparsity by employing a specialized loss function that aligns with the binomial distribution of single-cell data.
“For instance, in scRNA-seq analysis, zero-inflated autoencoders were introduced to handle data sparsity by employing a specialized loss function that aligns with the binomial distribution of single-cell data [33].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [33]
The Online Methods (Modeling choices; page not specified) support using a zero-inflated likelihood to model sparsity in scRNA-seq data. But the paper specifies a zero-inflated negative binomial distribution, not a binomial distribution; its separate zero-inflation component is Bernoulli. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMulti-omics integration is a natural application area where specialized agents analyze transcriptomic, epigenomic, proteomic, and spatial datasets, reconciling heterogeneous signals through shared representation spaces.
“Multi-omics integration represents another natural application area, where specialized agents analyze transcriptomic, epigenomic, proteomic, and spatial datasets, reconciling heterogeneous signals through shared representation spaces, a task particularly suited to foundation models capable of cross-modal alignment [12, 43, 44].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [43]
The Results section describes a low-dimensional mRNA latent space and compares cell similarity in that space with protein measurements. The Discussion says scVI could harmonize multiple datasets from a tissue. Neither passage describes specialized agents, integration of epigenomic or spatial data, or foundation-model cross-modal alignment; the protein measurements are used for evaluation, not integrated into the representation. (LLM, conf=0.88)
Experimental — accuracy not yet measured125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping.
“To map SEA-AD data to cell types consistently across all 84 donors, we first defined robust transcriptional types, named supertypes, in the BICCN reference; 125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping47,48(Extended Data Fig. 5a,b).”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [47]
The Introduction describes scVI as a hierarchical Bayesian model for single-cell transcriptomics, supporting the citation’s description of the probabilistic method. The Discussion says merging datasets while integrating cell-type annotations is a possible extension, not a reference-mapping result demonstrated in this paper. The 125 supertypes and mean F1 score appear to be the citing authors’ results, rather than results attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model.
“Importantly, statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model [10, 15].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [10]
Transcriptional state of individual cells may vary in ways poorly captured in pseudobulk.
“On the other hand, the transcriptional state of individual cells may vary in ways that are poorly captured in pseudo-bulk (for example, due to the presence of multiple cell types)38.”
From Transcriptome-wide analysis of differential expression in perturbation atlases (2025) · cites this paper as [38]
Latent representations learned by frameworks such as single-cell variational inference can be used for batch correction, clustering, visualization and DGE analysis.
“Latent representations, as learned by frameworks such as single-cell variational inference41, can be used for batch correction, clustering, visualization and DGE analysis.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [41]
Recently, deep learning has been used to produce an embedded workflow that can fit the data, denoise it and perform downstream analysis such as clustering and differential expression within the framework of the model.
“Recently, deep learning has been used to produce an embedded workflow that can fit the data, denoise it and perform downstream analysis such as clustering and differential expression within the framework of the model (scVI: Lopez et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Lopez et al, 2018
Current proposed single-cell foundation models produce cell embeddings intended to project noisy gene expression measurements to a more biologically relevant latent space and improve cell type resolution.
“Current proposed single-cell foundation models produce cell embeddings. These embeddings are intended to project potentially noisy gene expression measurements to a more biologically relevant latent space and to thus improve our ability to resolve cell types, consistent with previous machine learning methods in this field (including scVI) [30, 31, 24, 32].”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [24]
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SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
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- Authors
- Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Yosef, Nir
- Venue
- Nature Methods
- Published
- 2018-11-30
- Volume
- 15
- Issue
- 12
- Pages
- 1053-1058
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6289068
- OpenAlex
- W2901677030
- Cited by
- 2,530
- References
- 55
- Citations by year
- 2026: 588 · 2025: 489 · 2024: 409 · 2023: 311 · 2022: 248 · 2021: 239 · 2020: 155 · 2019: 80 · 2018: 11
- FWCI
- 43.21
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México
- National Institute of Allergy and Infectious Diseases (R38AI140299, U19AI090023)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- single-cell transcriptomics, single-cell RNA sequencing, deep generative models, batch effect correction, differential gene expression, cell clustering, uncertainty quantification
- MeSH terms
- Algorithms, Animals, Brain, Hematopoietic Stem Cells, Humans, Leukocytes, Mononuclear, Models, Biological, Genetic Variation, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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