Cited by 702Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

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Xu, Chenling, Lopez, Romain, Mehlman, Edouard, Regier, Jeffrey, Jordan, Michael I, Yosef, Nir·2021·Molecular Systems Biology

DOI 10.15252/msb.20209620PMC PMC7829634

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Abstract

As the number of single‐cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA‐seq data, while accounting for uncertainty caused by biological and measurement noise. We also introduce single‐cell ANnotation using Variational Inference (scANVI), a semi‐supervised variant of scVI designed to leverage existing cell state annotations. We demonstrate that scVI and scANVI compare favorably to state‐of‐the‐art methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings. In contrast to existing methods, scVI and scANVI integrate multiple datasets with a single generative model that can be directly used for downstream tasks, such as differential expression. Both methods are easily accessible through scvi‐tools.

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Method
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Trajectory-centric framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene modules in osteogenesis
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“For this, we restricted the data in both studies to the intersection of antibodies and used scANVI (83, 84) to transfer the labels.”
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“…in our proposed methodology couldbeseamlesslyintegrated intoexistingsingle-cellannotation procedures, we selected three exemplary methods: scmap hinged on traditional statistical learning [11], scANVI leveraged on deep representation learning [13] and CIForm based on transformer structure [49].”
BackgroundMethod
ACE: Explaining cluster from an adversarial perspectivePreprint
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MethodInfluential
Scaling deep identifiable models enables zero-shot characterization of single-cell biological statesPreprint
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“The generative process of scShift mostly aligned with the scvi-tools framework [10, 65], with the primary difference being the perturbation latent variable s i .”
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Adaptive Responses to PARP Inhibition Predict Response to Olaparib and Durvalumab: Multi-omic Analysis of Serial Biopsies in the AMTEC TrialPreprint
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“A central use of these reference models has also been to gain insights into new data using transfer learning 5–9 , similar to genome references in genomics.”
scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstructionPreprint
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ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian BrainPreprint
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Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism
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scCCTR: An iterative selection-based semi-supervised clustering model for single-cell RNA-seq data
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TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining
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The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies
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Cross‐Analysis of Single‐Cell Transcriptomic Datasets Reveals Conserved Neurogenic Gene Signatures and New Insights Into Neural Stem Cell Aging
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scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data
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Stc1-expressing myofibroblasts are a developmentally distinct lineage cleared through apoptosis in the neonatal lung
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Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace
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Decoding Human Placental Cellular and Molecular Responses to Obesity and Fetal Growth
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Single-cell atlas of AML reveals age-related gene regulatory networks in t(8;21) AML
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Increased expression of CD36 and CD163 in clear cell renal cell carcinoma suggests an association between lipid transport and an “M2-like” macrophage phenotype
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scDenorm: a denormalization tool for integrating single-cell transcriptomics data
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Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
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InfluentialSelf-citation
A Hormone Cell Atlas maps the human endocrine system at cellular resolution
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High Content Single Cell and Spatial Tissue Profiling Modalities for Deciphering the Pathogenesis and Treatment of Wound Healing
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scE(match): Privacy-Preserving Cluster Matching of Single-Cell Data
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Fibroblasts Are the Primary Contributors to a Disrupted Micro-Environment in End-Stage Pediatric Hypertrophic Cardiomyopathy
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ArchMap is a web-based platform for reference-based analysis of single-cell datasets
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A deep generative framework with embedded vector arithmetic and classifier for sample generation, label transfer, and clustering of single-cell data
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BackgroundInfluential
PLAE Web App Enables Powerful Searching and Multiple Visualizations Across One Million Unified Single-Cell Ocular Transcriptomes
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NF-κB inhibitor alpha has a cross-variant role during SARS-CoV-2 infection in ACE2-overexpressing human airway organoidsPreprint
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Boosting Single-Cell RNA Sequencing Analysis with Simple Neural AttentionPreprint
Davalos, O. A., Heydari, A. A., Fertig, E. J., Sindi, S., & Hoyer, K. K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.29.542760 · Cited by 2
Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human DiseasePreprint
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Deep Learning Based Models for Preimplantation Mouse and Human DevelopmentPreprint
Proks, M., Salehin, N., & Brickman, J. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.16.580649 · Cited by 2
“We therefore chose, to continue with scVI for integration and scANVI (Xu, 2021) for all cell type classification.”
BackgroundMethod

Authors: Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N.
Affiliations: University of California, Berkeley (US)
École Polytechnique (FR)
University of Michigan (US)
Harvard University (US)
Corresponding author: Yosef, Nir
ORCID: Xu, Chenling
Lopez, Romain
Mehlman, Edouard
Regier, Jeffrey
Jordan, Michael I
Yosef, Nir
Venue: Molecular Systems Biology
Publisher: Springer Science and Business Media LLC
Published: 2021-01-01
Volume: 17
Issue: 1
Pages: e9620-e9620
ISSN: 1744-4292 (ISSN-L)
DOI: 10.15252/msb.20209620
PMC: PMC7829634
OpenAlex: W3121168830
Semantic Scholar: 58c22899af553a611bbd3acfd8d2b43f86d2b3fe
Cited by: 691
References: 83
Citations by year: 2026: 206 · 2025: 188 · 2024: 126 · 2023: 91 · 2022: 44 · 2021: 29
FWCI: 23.55
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 24 d)
Locations: Molecular Systems Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: HHS | NIH | National Institute of Allergy and Infectious Diseases (U19 AI090023)
NIH | National Institute of Mental Health (U19 MH114821)
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 Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: single-cell transcriptomics, data integration, cell type annotation, scVI, scANVI, variational inference, deep generative models, stochastic modeling, uncertainty quantification, semi-supervised learning, differential gene expression
MeSH terms: Supervised Machine Learning, Humans, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Databases, Genetic, Molecular Sequence Annotation, Single-Cell Analysis

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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

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