Cobolt: integrative analysis of multimodal single-cell sequencing data

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Abstract

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-021-02556-z).

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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Fast unfolding of communities in large networks

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte … (2008)

Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008

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Author and article information

Contributors

Boying Gong: boyinggong@berkeley.edu

Yun Zhou: yzhou277@berkeley.edu

Elizabeth Purdom:

ORCID: http://orcid.org/0000-0001-9455-7990

epurdom@stat.berkeley.edu

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 28 December 2021

Publication date PMC-release: 28 December 2021

Publication date Collection: 2021

Volume: 22

Electronic Location Identifier: 351

Affiliations

[1 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Division of Biostatistics, , University of California, Berkeley, ; Berkeley, CA USA

[2 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Statistics, , University of California, Berkeley, ; Berkeley, CA USA

Author information

Elizabeth Purdom http://orcid.org/0000-0001-9455-7990

Article

Publisher ID: 2556

DOI: 10.1186/s13059-021-02556-z

PMC ID: 8715620

PubMed ID: 34963480

SO-VID: ec4f2c18-ada5-4fdd-84a5-cbd016e0b6c1

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 15 July 2021

Date accepted : 22 November 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: U19MH114830

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ScienceOpen disciplines: Genetics

Keywords: single cell,multi-omics,integration

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ScienceOpen disciplines: Genetics

Keywords: single cell, multi-omics, integration

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Cobolt: integrative analysis of multimodal single-cell sequencing data

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Genome Integrity

Most cited references 41

Comprehensive Integration of Single-Cell Data

Integrated analysis of multimodal single-cell data

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