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Single-cell mRNA quantification and differential analysis with Census

Nature Methods volume 14, pages 309–315 (2017)Cite this article

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Abstract

Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.

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Figure 1: Census approximation of relative transcript counts in single cells without external RNA standards.
Figure 2: Census counts improved the accuracy of differential expression analysis.
Figure 3: BEAM identification of branch-dependent gene expression and potential drivers of lung epithelial fate specification.
Figure 4: Loss of interferon signaling generated a branch in the trajectory followed by immune-stimulated dendritic cells.
Figure 5: Census enabled robust analysis of differential splicing during human myoblast differentiation.
Figure 6: Census detected shifts in allelic balance in single cells during embryogenesis.

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Acknowledgements

We thank J. Shi and S. Xu for technical discussions, M. Kircher for cluster computation support, and J. Shendure, R. Hause, D. Cusanovich, B. Trapnell, J. Whitsett and members of the Trapnell laboratory for comments on the manuscript. This work was supported by US National Institutes of Health (NIH) grant DP2 HD088158. C.T. is partly supported by a Dale. F. Frey Award for Breakthrough Scientists and an Alfred P. Sloan Foundation Research Fellowship. A.H. is supported by a National Science Foundation (NSF) Graduate Research Fellowship.

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Xiaojie Qiu, Andrew Hill, Jonathan Packer, Dejun Lin & Cole Trapnell

  2. Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, USA

    Xiaojie Qiu & Cole Trapnell

  3. Department of Applied Mathematics, University of Washington, Seattle, Washington, USA

    Yi-An Ma

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Contributions

X.Q. and C.T. designed Census and the regression methods. X.Q. implemented the methods. X.Q. and A.H. performed the analysis. J.P., D.L. and Y.-A.M. contributed to technical design. C.T. conceived the project. All authors wrote the manuscript.

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Correspondence to Cole Trapnell.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Table 2 and Supplementary Note 1 (PDF 17987 kb)

Supplementary Tables

Supplementary Table 1 (XLSX 166 kb)

Supplementary Data

Text file storing the result (p-value) from the permutation test used in benchmarking differential gene expression based on spike-in transcript counts. Each row corresponds to a gene. (TXT 1113 kb)

Supplementary Software

A tarball includes a version of monocle 2 (version: 1.99) used to produce all the figures, supplementary data is provided along with this submission and a helper package including helper functions are included as well as all analysis code which can reproduce all figures in this study are provided. (ZIP 6397 kb)

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Qiu, X., Hill, A., Packer, J. et al. Single-cell mRNA quantification and differential analysis with Census. Nat Methods 14, 309–315 (2017). https://doi.org/10.1038/nmeth.4150

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