Cited by 592Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

Joint probabilistic modeling of single-cell multi-omic data with totalVI

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Gayoso, Adam, Steier, Zoë, Lopez, Romain, Regier, Jeffrey, Nazor, Kristopher L., Streets, Aaron, Yosef, Nir·2021·Nature Methods

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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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Research works printed but not linked: 2

  1. [63]Sønderby CK, Raiko T, Maaløe L, Sønderby SK & Winther O Ladder variational autoencoders. in Neural information processing systems (2016).
  2. [91]Burda Y, Grosse R & Salakhutdinov R Importance weighted Autoencoders. in International conference on learning representations (2016).

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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
Venue
Nature Methods
Published
2021-02-15
Volume
18
Issue
3
Pages
272-282
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
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
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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