Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

doi:10.1038/nbt.3102

. 2015 Feb;33(2):155-60.

doi: 10.1038/nbt.3102. Epub 2015 Jan 19.

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Florian Buettner¹, Kedar N Natarajan², F Paolo Casale³, Valentina Proserpio², Antonio Scialdone², Fabian J Theis⁴, Sarah A Teichmann², John C Marioni², Oliver Stegle³

Affiliations

¹ 1] Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. [2] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
² 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. [2] Wellcome Trust Sanger Institute, Hinxton, UK.
³ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
⁴ 1] Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. [2] Department of Mathematics, Technische Universität München, Munich, Germany.

PMID: 25599176
DOI: 10.1038/nbt.3102

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Florian Buettner et al. Nat Biotechnol. 2015 Feb.

. 2015 Feb;33(2):155-60.

doi: 10.1038/nbt.3102. Epub 2015 Jan 19.

Authors

Florian Buettner¹, Kedar N Natarajan², F Paolo Casale³, Valentina Proserpio², Antonio Scialdone², Fabian J Theis⁴, Sarah A Teichmann², John C Marioni², Oliver Stegle³

Affiliations

¹ 1] Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. [2] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
² 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. [2] Wellcome Trust Sanger Institute, Hinxton, UK.
³ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
⁴ 1] Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. [2] Department of Mathematics, Technische Universität München, Munich, Germany.

PMID: 25599176
DOI: 10.1038/nbt.3102

Abstract

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

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Comment in

Kindred cells among the crowd.
Nawy T. Nawy T. Nat Methods. 2015 Mar;12(3):170-1. doi: 10.1038/nmeth.3307. Nat Methods. 2015. PMID: 25879100 No abstract available.
The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.
McDavid A, Finak G, Gottardo R. McDavid A, et al. Nat Biotechnol. 2016 Jun 9;34(6):591-3. doi: 10.1038/nbt.3498. Nat Biotechnol. 2016. PMID: 27281413 Free PMC article. No abstract available.
Reply to The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.
[No authors listed] [No authors listed] Nat Biotechnol. 2016 May 6;34(6):593-5. doi: 10.1038/nbt.3607. Nat Biotechnol. 2016. PMID: 27281414 No abstract available.

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Selective single cell isolation for genomics using microraft arrays.
Welch JD, Williams LA, DiSalvo M, Brandt AT, Marayati R, Sims CE, Allbritton NL, Prins JF, Yeh JJ, Jones CD. Welch JD, et al. Nucleic Acids Res. 2016 Sep 30;44(17):8292-301. doi: 10.1093/nar/gkw700. Epub 2016 Aug 16. Nucleic Acids Res. 2016. PMID: 27530426 Free PMC article.
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References

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[1] Stem Cell Reports. 2013 Dec 05;1(6):532-44 - PubMed

[2] Stem Cell Reports. 2013 Dec 05;1(6):532-44 - PubMed

[3] Nat Rev Genet. 2015 Mar;16(3):133-45 - PubMed

[4] Nat Rev Genet. 2015 Mar;16(3):133-45 - PubMed

[5] Anal Chem. 2003 Sep 15;75(18):4718-23 - PubMed

[6] Anal Chem. 2003 Sep 15;75(18):4718-23 - PubMed

[7] Anal Chem. 2003 Jul 15;75(14):3581-6 - PubMed

[8] Anal Chem. 2003 Jul 15;75(14):3581-6 - PubMed

[9] Cell Stem Cell. 2010 May 7;6(5):468-78 - PubMed

[10] Cell Stem Cell. 2010 May 7;6(5):468-78 - PubMed

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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

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