A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

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Abstract

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.

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The online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users.

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Most cited references 41

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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Stochasticity in gene expression: from theories to phenotypes.

Mads Kaern, Timothy C. Elston, William Blake … (2005)

Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.

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Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Alex Shalek, Rahul Satija, Xian Adiconis … (2013)

Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

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Author and article information

Contributors

Keegan D. Korthauer: keegan@jimmy.harvard.edu

Li-Fang Chu: LChu@morgridge.org

Michael A. Newton: newton@biostat.wisc.edu

Yuan Li: yuanli@stat.wisc.edu

James Thomson: jthomson@morgridgeinstitute.org

Ron Stewart: RStewart@morgridge.org

Christina Kendziorski: kendzior@biostat.wisc.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): 25 October 2016

Publication date PMC-release: 25 October 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 222

Affiliations

[1 ]Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, 02215 MA USA

[2 ]Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, 02115 MA USA

[3 ]Morgridge Institute for Research, University of Wisconsin, Madison, 53706 WI USA

[4 ]Department of Biostatistics, University of Wisconsin, Madison, 53706 WI USA

[5 ]Department of Statistics, University of Wisconsin, Madison, 53706 WI USA

[6 ]Department of Cell and Regenerative Biology, University of Wisconsin, Madison, 53706 WI USA

[7 ]Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, 93106 CA USA

Article

Publisher ID: 1077

DOI: 10.1186/s13059-016-1077-y

PMC ID: 5080738

PubMed ID: 27782827

SO-VID: 3a1215e5-9f1f-43dd-a8f1-5a3d01bc544a

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

History

Date received : 4 August 2016

Date accepted : 4 October 2016

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: GM102756

Award Recipient : Christina Kendziorski

Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;

Award ID: U54AI117924

Award Recipient : Christina Kendziorski

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

Award ID: 4UH3TR000506-03

Award Recipient : James Thomson

Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;

Award ID: 5U01HL099773-06

Award Recipient : James Thomson

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: single-cell rna-seq,differential expression,cellular heterogeneity,mixture modeling

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ScienceOpen disciplines: Genetics

Keywords: single-cell rna-seq, differential expression, cellular heterogeneity, mixture modeling

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

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Most cited references 41

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Stochasticity in gene expression: from theories to phenotypes.

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

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