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scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Author

Listed:
  • Katharina T. Schmid

    (Helmholtz Zentrum München – German Research Center for Environmental Health
    Technical University Munich)

  • Barbara Höllbacher

    (Helmholtz Zentrum München – German Research Center for Environmental Health
    Technical University Munich)

  • Cristiana Cruceanu

    (Max Planck Institute for Psychiatry)

  • Anika Böttcher

    (Helmholtz Diabetes Center, Helmholtz Zentrum München – German Research Center for Environmental Health
    German Center for Diabetes Research (DZD)
    Technical University of Munich)

  • Heiko Lickert

    (Helmholtz Diabetes Center, Helmholtz Zentrum München – German Research Center for Environmental Health
    German Center for Diabetes Research (DZD)
    Technical University of Munich)

  • Elisabeth B. Binder

    (Max Planck Institute for Psychiatry
    Emory University School of Medicine)

  • Fabian J. Theis

    (Helmholtz Zentrum München – German Research Center for Environmental Health
    Technical University Munich)

  • Matthias Heinig

    (Helmholtz Zentrum München – German Research Center for Environmental Health
    Technical University Munich)

Abstract

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Suggested Citation

  • Katharina T. Schmid & Barbara Höllbacher & Cristiana Cruceanu & Anika Böttcher & Heiko Lickert & Elisabeth B. Binder & Fabian J. Theis & Matthias Heinig, 2021. "scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26779-7
    DOI: 10.1038/s41467-021-26779-7
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    References listed on IDEAS

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    2. Xiaoying Wang & Maoteng Duan & Jingxian Li & Anjun Ma & Gang Xin & Dong Xu & Zihai Li & Bingqiang Liu & Qin Ma, 2024. "MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Amélie Roehrig & Theo Z. Hirsch & Aurore Pire & Guillaume Morcrette & Barkha Gupta & Charles Marcaillou & Sandrine Imbeaud & Christophe Chardot & Emmanuel Gonzales & Emmanuel Jacquemin & Masahiro Seki, 2024. "Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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