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A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Nature Methods volume 16, pages 163–166 (2019)Cite this article

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Matters Arising to this article was published on 01 June 2020

Abstract

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes.

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Fig. 1: Logistic regression applied to scRNA-seq.
Fig. 2: Logistic regression identifies CD45 in purified T cell types.

Code availability

The code required to conduct the simulations and reproduce the analyses is available at https://github.com/pachterlab/NYMP_2018. We also have provided the Github repository that was zipped at the time of manuscript acceptance as Supplementary Software.

Data availability

The myogenesis dataset (Trapnell et al.10) is available on the conquer database and on GEO as series GSE52529. The dataset on embryogenesis is available on the conquer database (Petropoulos et al.22). The 10x PBMC dataset is available from the 10x Genomics Support website19.

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Acknowledgements

We thank N. Bray, J. Gehring and V. Svensson for discussion and comments on the manuscript, and H. Pimentel for assisting with the simulations. We thank A. Butler and R. Satija for implementing this method in Seurat. V.N., L.Y. and L.P. are partially funded by NIH R012017-0569.

Author information

Author notes

  1. These authors contributed equally: Vasilis Ntranos, Lynn Yi.

Authors and Affiliations

  1. Department of Electrical Engineering & Computer Science, UC Berkeley, Berkeley, CA, USA

    Vasilis Ntranos

  2. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Vasilis Ntranos

  3. UCLA–Caltech Medical Science Training Program, UCLA, Los Angeles, CA, USA

    Lynn Yi

  4. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Lynn Yi & Lior Pachter

  5. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland

    Páll Melsted

  6. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

Authors
  1. Vasilis Ntranos
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  2. Lynn Yi
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  3. Páll Melsted
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  4. Lior Pachter
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Contributions

V.N. developed the model during discussions with L.Y. and L.P, and analyzed the 10x PBMC dataset. L.Y. performed the simulations and analyzed the embryo SMART-Seq dataset. P.M. developed kallisto genomebam and assisted with analysis. All authors contributed extensively to the interpretation of the results and writing of the manuscript.

Corresponding author

Correspondence to Lior Pachter.

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The authors declare no competing interests.

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Ntranos, V., Yi, L., Melsted, P. et al. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat Methods 16, 163–166 (2019). https://doi.org/10.1038/s41592-018-0303-9

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