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A general and flexible method for signal extraction from single-cell RNA-seq data

Author

Listed:
  • Davide Risso

    (Weill Cornell Medicine)

  • Fanny Perraudeau

    (University of California)

  • Svetlana Gribkova

    (Université Paris Diderot)

  • Sandrine Dudoit

    (University of California
    University of California)

  • Jean-Philippe Vert

    (PSL Research University
    Institut Curie
    INSERM U900
    Ecole Normale Supérieure)

Abstract

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

Suggested Citation

  • Davide Risso & Fanny Perraudeau & Svetlana Gribkova & Sandrine Dudoit & Jean-Philippe Vert, 2018. "A general and flexible method for signal extraction from single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02554-5
    DOI: 10.1038/s41467-017-02554-5
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    Cited by:

    1. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    2. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Qi Liu & Charles A Herring & Quanhu Sheng & Jie Ping & Alan J Simmons & Bob Chen & Amrita Banerjee & Wei Li & Guoqiang Gu & Robert J Coffey & Yu Shyr & Ken S Lau, 2018. "Quantitative assessment of cell population diversity in single-cell landscapes," PLOS Biology, Public Library of Science, vol. 16(10), pages 1-29, October.
    4. Yue Cao & Pengyi Yang & Jean Yee Hwa Yang, 2021. "A benchmark study of simulation methods for single-cell RNA sequencing data," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Xiaotian Wu & Hao Wu & Zhijin Wu, 2021. "Penalized Latent Dirichlet Allocation Model in Single-Cell RNA Sequencing," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 543-562, December.

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