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Aggregating transcript-level analyses for single-cell differential gene expression

Nature Methods volume 17, pages 583–585 (2020)Cite this article

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The Original Article was published on 21 January 2019

arising from: Ntranos, V. et al. Nature Methods https://www.nature.com/articles/s41592-018-0303-9 (2019).

In their recent article, Ntranos et al.1 propose multivariate logistic regression (mLR) as a way to perform gene differential expression (GDE) on single-cell RNA-sequencing (scRNAseq) data summarized at the transcript level. We agree with Ntranos et al. that transcript-level analyses can identify differential expression events, such as isoform-switching events, that a gene-level analysis may be unable to reveal. However, we highlight several omissions and errors that we believe unfairly penalized previously published approaches in the report by Ntranos et al. We have performed our own analysis, which suggests that these approaches perform just as well if not better than logistic regression, while being more flexible in terms of modeling.

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Fig. 1: Comparison of multivariate logistic regression with other differential expression methods combined with P-value aggregation using the Šidák method.

Data availability

A list of datasets and accession links is available in Supplementary Table 2. Information about simulated datasets is available in Supplementary Table 3. Simulated datasets are available at https://figshare.com/articles/Single_cell_differential_isoform_expression/12210254 (https://doi.org/10.6084/m9.figshare.12210254).

Code availability

Most of the analysis presented here are extensions of the analysis performed in the Ntranos et al. article1. The original code is available at https://github.com/pachterlab/NYMP_2018. Specifically, Fig. 1b is adapted from the code available at https://github.com/pachterlab/NYMP_2018/tree/master/10x_example-logR, while Fig. 1a is adapted from code available at https://github.com/pachterlab/NYMP_2018/tree/master/simulations. Figures 1c and 1d are adapted from code available at https://github.com/pachterlab/NYMP_2018/tree/master/embryo. The adapted and new code is available at https://github.com/ebecht/logistic_regresion_for_GDE.

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Acknowledgements

This research was funded by the National Institutes of Health Human Immunology Project Consortium (U19AI128914) and the Vaccine and Immunology Statistical Center (Bill and Melinda Gates Foundation grant no. OPP1032317). R.A. was funded by a postdoctoral fellowship from the Fred Hutch Immunotherapy Integrated Research Center.

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Authors and Affiliations

  1. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Etienne Becht, Edward Zhao, Robert Amezquita & Raphael Gottardo

  2. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Edward Zhao & Raphael Gottardo

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  1. Etienne Becht
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  2. Edward Zhao
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Contributions

All the authors participated in analyzing the data. E.B. and R.G. wrote the manuscript. All authors reviewed the manuscript. R.G. led the study.

Corresponding author

Correspondence to Raphael Gottardo.

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Competing interests

R.G. declares ownership in CellSpace Biosciences. The other authors do not declare any competing interest.

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Peer review information Allison Doerr and Nicole Rusk were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Becht, E., Zhao, E., Amezquita, R. et al. Aggregating transcript-level analyses for single-cell differential gene expression. Nat Methods 17, 583–585 (2020). https://doi.org/10.1038/s41592-020-0854-4

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