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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
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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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“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 [48]
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Defining cell-type specificity at the transcriptional level in human diseasePreprint
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“S100a8 is a neutrophil specific gene predicted by Nano-dissection [47] and is associated with GO processes such as leukocyte migration associated with inflammation and neutrophil aggregation.”
BackgroundFunctional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 259 · cited 1×
“The first is ab initio labeling of cells based on marker genes or gene signatures (DeTo-maso & Yosef, 2016; Butler et al , 2018; DeTomaso et al , 2019).”
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Single cells make big data: New challenges and opportunities in transcriptomics
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“We explore three such cases, including a bone marrow 10x and Smart-Seq2 pair from the Tabula Muris project (MarrowTM-10x, MarrowTMss2; Schaum et al , 2018), a pancreas inDrop and CEL-Seq2 pair (Pan-creas-InDrop, PancreasCEL-Seq2; Baron et al , 2016), and a dentate gyrus 10x and Fluidigm C1 pair (DentateGyrus10x, Denta…”
Hierarchical Multi-Label Classification Networks
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Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells
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Simulating multiple faceted variability in single cell RNA sequencing
Zhang, X., Xu, C. A., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-10500-w · Cited by 124
Self-citationBenchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122
Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105 · cited 1×
“…Stuart et al , 2019), further work can utilize probabilistic graphical models that quantify measurement uncertainties in each assay, as well as the uncertainties of transferring information between modalities (e.g., predicting unmeasured gene expression in smFISH data as in (Lopez et al , 2019)).”
Self-citationThe human reticulocyte transcriptome
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“For instance, the HEMATO-Tusi unique erythroid cell population expresses Hba-a2 (hemoglobin subunit) and Alas2 (erythroid-specific mitochondrial 5-aminolevulinate synthase) that are known to be present in reticulocytes (Goh et al , 2007, MTA, 2018).”
Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq dataPreprint
Wagner, F., & Yanai, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/456129 · Cited by 73 · cited 4×
“…task is often complicated by factors such as differences in technology (e.g., using Smart-Seq2 data to annotate 10x Chromium data), partial overlap in cell type composition (i.e., not all labels should be transferred and not all unannotated cells should be assigned a label), complex organization…”
InfluentialHu, Q., & Greene, C. S. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0033 · Cited by 68
A guide to the BRAIN Initiative Cell Census Network data ecosystem
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FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
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“One concern in applying methods based on neural networks (Ding et al , 2018; Wang & Gu, 2018; Amodio et al , 2019; Eraslan et al , 2019; Grønbech et al , 2020) in single-cell genomics and other domains is the robustness to hyperparameters choices (Hu & Greene, 2019).”
Self-citationBoosting the power of single-cell analysis
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MAGAN: Aligning Biological ManifoldsPreprint
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“We further discuss work related to harmonization of scRNA-seq data, as well as machine learning research in domain adaptation (from which most of these methods, including ours, built upon) in Appendix Note A.”
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Dictionary learning for integrative, multimodal and scalable single-cell analysis
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“This strategy is enabled by the curation and public release of reference datasets as well as the development of new computational tools, including statistical learning 4–7 and deep learning-based approaches 8,9 , that have been successfully applied toward this goal.”
Benchmarking atlas-level data integration in single-cell genomics
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“2–15), we found that the varying complexity of tasks affects the ranking of integration methods: while Seurat v3 and Harmony perform well on simpler real data tasks and some simulations, Scanorama and scVI performed particularly well on more complex real data.”
BackgroundMethodBest practices for single-cell analysis across modalities
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“S1 to S16 Tables S1 to S9 References (40-95)”
BackgroundSelf-citationAn integrated cell atlas of the lung in health and disease
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Self-citation
Metadata
- 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
- Venue
- Molecular Systems Biology
- Published
- 2021-01-01
- Volume
- 17
- Issue
- 1
- Pages
- e9620-e9620
- ISSN
- 1744-4292 (ISSN-L)
- 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
- 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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