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

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 repr...

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Bibliographic Details
Published inNature methods Vol. 18; no. 3; pp. 272 - 282
Main Authors Gayoso, Adam, Steier, Zoë, Lopez, Romain, Regier, Jeffrey, Nazor, Kristopher L., Streets, Aaron, Yosef, Nir
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.03.2021
Nature Publishing Group
Subjects
631/114
631/114/1305
631/114/2397
631/114/2401
631/250
Animals
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cells, Cultured
Correlation analysis
Data Analysis
Epitopes
Female
Gene expression
High-Throughput Screening Assays - methods
Immune system
Inference
Life Sciences
Lymph nodes
Lymph Nodes - cytology
Lymph Nodes - metabolism
Measurement
Membrane proteins
Methods
Mice
Mice, Inbred C57BL
Phenotypes
Phenotypic variations
Probabilistic models
Proteins
Proteins - analysis
Proteomics
RNA - analysis
RNA - genetics
RNA sequencing
Single-Cell Analysis - methods
Spleen
Spleen - cytology
Spleen - metabolism
Transcription
Transcriptome - genetics
Transcriptomes
United States
Online AccessGet full text
ISSN1548-7091
1548-7105
1548-7105
DOI10.1038/s41592-020-01050-x

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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. Total Variational Inference is a framework for end-to-end analysis of paired transcriptome and protein measurements such as CITE-seq data in single cells.
AbstractList 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.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.
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.
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; Total Variational Inference is a framework for end-to-end analysis of paired transcriptome and protein measurements such as CITE-seq data in single cells.
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.Total Variational Inference is a framework for end-to-end analysis of paired transcriptome and protein measurements such as CITE-seq data in single cells.
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. Total Variational Inference is a framework for end-to-end analysis of paired transcriptome and protein measurements such as CITE-seq data in single cells.
The paired measurement of RNA and surface proteins in single cells with 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 like dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules, and differential expression testing.
Audience Academic
Author Nazor, Kristopher L.
Steier, Zoë
Streets, Aaron
Lopez, Romain
Regier, Jeffrey
Gayoso, Adam
Yosef, Nir
AuthorAffiliation 5 BioLegend, Inc., San Diego, CA, USA
7 Ragon Institute of MGH, MIT and Harvard
6 Chan Zuckerberg Biohub, San Francisco, CA, USA
3 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
1 Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
2 Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
4 Department of Statistics, University of Michigan, Ann Arbor, Ann Arbor, MI, USA
AuthorAffiliation_xml – name: 5 BioLegend, Inc., San Diego, CA, USA
– name: 7 Ragon Institute of MGH, MIT and Harvard
– name: 2 Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
– name: 3 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
– name: 1 Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
– name: 6 Chan Zuckerberg Biohub, San Francisco, CA, USA
– name: 4 Department of Statistics, University of Michigan, Ann Arbor, Ann Arbor, MI, USA
Author_xml – sequence: 1
  givenname: Adam
  orcidid: 0000-0001-9537-0845
  surname: Gayoso
  fullname: Gayoso, Adam
  organization: Center for Computational Biology, University of California, Berkeley
– sequence: 2
  givenname: Zoë
  orcidid: 0000-0003-1488-7995
  surname: Steier
  fullname: Steier, Zoë
  organization: Department of Bioengineering, University of California, Berkeley
– sequence: 3
  givenname: Romain
  orcidid: 0000-0003-0495-738X
  surname: Lopez
  fullname: Lopez, Romain
  organization: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
– sequence: 4
  givenname: Jeffrey
  orcidid: 0000-0002-1472-5235
  surname: Regier
  fullname: Regier, Jeffrey
  organization: Department of Statistics, University of Michigan, Ann Arbor
– sequence: 5
  givenname: Kristopher L.
  surname: Nazor
  fullname: Nazor, Kristopher L.
  organization: BioLegend, Inc
– sequence: 6
  givenname: Aaron
  orcidid: 0000-0002-3909-8389
  surname: Streets
  fullname: Streets, Aaron
  email: astreets@berkeley.edu
  organization: Center for Computational Biology, University of California, Berkeley, Department of Bioengineering, University of California, Berkeley, Chan Zuckerberg Biohub
– sequence: 7
  givenname: Nir
  orcidid: 0000-0001-9004-1225
  surname: Yosef
  fullname: Yosef, Nir
  email: niryosef@berkeley.edu
  organization: Center for Computational Biology, University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Chan Zuckerberg Biohub, Ragon Institute of MGH, MIT and Harvard
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33589839$$D View this record in MEDLINE/PubMed
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These authors contributed equally.
A.G. and Z.S. contributed equally. A.G., Z.S., A.S., and N.Y. designed the study. A.G., Z.S, R.L., J.R., and N.Y. conceived of the statistical model. A.G. implemented the totalVI software with input from R.L. K.L.N. designed and produced antibody panels and provided input on the study. Z.S. designed and led experiments with input from A.S. and N.Y. A.G. and Z.S. designed and implemented analysis methods and applied the software to analyze the data with input from A.S. and N.Y. A.S. and N.Y. supervised the work. A.G., Z.S., R.L., J.R., A.S., and N.Y. participated in writing the manuscript.
Author contributions
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0000-0001-9004-1225
0000-0001-9537-0845
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SSID ssj0033425
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Snippet The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a...
The paired measurement of RNA and surface proteins in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell...
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pubmed
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springer
SourceType Open Access Repository
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Index Database
Enrichment Source
Publisher
StartPage 272
SubjectTerms 631/114
631/114/1305
631/114/2397
631/114/2401
631/250
Animals
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cells, Cultured
Correlation analysis
Data Analysis
Epitopes
Female
Gene expression
High-Throughput Screening Assays - methods
Immune system
Inference
Life Sciences
Lymph nodes
Lymph Nodes - cytology
Lymph Nodes - metabolism
Measurement
Membrane proteins
Methods
Mice
Mice, Inbred C57BL
Phenotypes
Phenotypic variations
Probabilistic models
Proteins
Proteins - analysis
Proteomics
RNA - analysis
RNA - genetics
RNA sequencing
Single-Cell Analysis - methods
Spleen
Spleen - cytology
Spleen - metabolism
Transcription
Transcriptome - genetics
Transcriptomes
Title Joint probabilistic modeling of single-cell multi-omic data with totalVI
URI https://link.springer.com/article/10.1038/s41592-020-01050-x
https://www.ncbi.nlm.nih.gov/pubmed/33589839
https://www.proquest.com/docview/2499377689
https://www.proquest.com/docview/2490122353
https://pubmed.ncbi.nlm.nih.gov/PMC7954949
Volume 18
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