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Validation of noise models for single-cell transcriptomics

Nature Methods volume 11, pages 637–640 (2014)Cite this article

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Abstract

Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.

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Figure 1: Analysis of gene expression noise with single-cell mRNA sequencing.
Figure 2: Modeling of technical variability and inference of biological noise.
Figure 3: Validation of predicted biological variability by smFISH.

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Gene Expression Omnibus

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Acknowledgements

This work was supported by a European Research Council Advanced grant (ERC-AdG 294325-GeneNoiseControl) and a Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Vici award.

Author information

Author notes

  1. Dominic Grün and Lennart Kester: These authors contributed equally to this work.

Authors and Affiliations

  1. Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands

    Dominic Grün, Lennart Kester & Alexander van Oudenaarden

  2. University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands

    Dominic Grün, Lennart Kester & Alexander van Oudenaarden

Authors
  1. Dominic Grün
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  2. Lennart Kester
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  3. Alexander van Oudenaarden
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Contributions

D.G., L.K. and A.v.O. conceived the methods. D.G. developed the noise models, performed all computations and wrote the manuscript. L.K. performed all experiments and corrected the manuscript. A.v.O. guided experiments, data analysis and writing of the manuscript, and corrected the manuscript.

Corresponding author

Correspondence to Alexander van Oudenaarden.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Table 1 and Supplementary Notes 1–4. (PDF 11686 kb)

Supplementary Table 2

GO terms enriched among genes with increased expression variability in serum versus 2i culture condition. Enriched biological processes and enriched molecular functions are given as separate lists. Only significantly enriched GO-terms (P < 0.05) were included. The lists indicate the GO-term ID, the hypergeometric P-value, the odds ratio, the expected number of genes associated with each GO-term, the observed number of genes for each GO-term, the size of the GO-term (total number of genes associated) and a short description. For the inference of over-represented GO terms, the set of differentially variable genes was compared to the universe of all genes expressed in the two conditions. The GOstats package was used to compute GO enrichment in R. (XLSX 82 kb)

Supplementary Table 3

Probe set composition of smFISH probes used. Each column represents a probe set for the gene specified in the column header. All probes were labeled on the 3' end with TMR, Alexa594 or Cy5. (XLSX 56 kb)

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Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat Methods 11, 637–640 (2014). https://doi.org/10.1038/nmeth.2930

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