Joint probabilistic modeling of single-cell multi-omic data with totalVI
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Abstract
The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.
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totalVI learns a joint probabilistic representation of paired measurements that accounts for noise and technical biases, including batch effects per modality.
“the CITE-seq specific, deep-learning-based totalVI162, which learns a joint probabilistic representation of paired measurements that also accounts for noise and technical biases, including batch effects per modality.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [162]
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BackgroundSpatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
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An immune world model for multiscale forecasting and therapeutic hypothesis generation
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- Authors
- Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- University of Michigan (US)
- BioLegend (United States) (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Streets, Aaron
- Yosef, Nir
- ORCID
- Venue
- Nature Methods
- Published
- 2021-02-15
- Volume
- 18
- Issue
- 3
- Pages
- 272-282
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7954949
- OpenAlex
- W3129866267
- Cited by
- 592
- References
- 142
- Citations by year
- 2026: 160 · 2025: 124 · 2024: 111 · 2023: 92 · 2022: 70 · 2021: 34 · 2020: 1
- FWCI
- 24.78
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Institute of Mental Health (U19MH114821)
- National Human Genome Research Institute (K22HG000047, T32HG000047)
- National Institute of General Medical Sciences (R35GM124916)
- National Institute of Allergy and Infectious Diseases (R24AI145816)
- 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
- CITE-seq, single-cell multi-omics, variational inference, scVI, surface proteins, batch effects, immune cells, dimensionality reduction, data integration, differential gene expression
- MeSH terms
- Data Analysis, Animals, Cells, Cultured, Female, Lymph Nodes, Mice, Inbred C57BL, Proteins, RNA, Spleen, Mice, High-Throughput Screening Assays, Single-Cell Analysis, Transcriptome
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