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Determining sequencing depth in a single-cell RNA-seq experiment

Author

Listed:
  • Martin Jinye Zhang

    (Stanford University)

  • Vasilis Ntranos

    (Stanford University
    California Institute of Technology)

  • David Tse

    (Stanford University)

Abstract

An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.

Suggested Citation

  • Martin Jinye Zhang & Vasilis Ntranos & David Tse, 2020. "Determining sequencing depth in a single-cell RNA-seq experiment," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14482-y
    DOI: 10.1038/s41467-020-14482-y
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    Cited by:

    1. Hai C. T. Nguyen & Bukyung Baik & Sora Yoon & Taesung Park & Dougu Nam, 2023. "Benchmarking integration of single-cell differential expression," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Nelson Johansen & Hongru Hu & Gerald Quon, 2023. "Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. A. S. Eisele & M. Tarbier & A. A. Dormann & V. Pelechano & D. M. Suter, 2024. "Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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