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

Katharina T. Schmid, Barbara Höllbacher, Cristiana Cruceanu, Anika Böttcher, Heiko Lickert, Elisabeth B. Binder, Fabian J. Theis and Matthias Heinig ()
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Katharina T. Schmid: Helmholtz Zentrum München – German Research Center for Environmental Health
Barbara Höllbacher: Helmholtz Zentrum München – German Research Center for Environmental Health
Cristiana Cruceanu: Max Planck Institute for Psychiatry
Anika Böttcher: Helmholtz Diabetes Center, Helmholtz Zentrum München – German Research Center for Environmental Health
Heiko Lickert: Helmholtz Diabetes Center, Helmholtz Zentrum München – German Research Center for Environmental Health
Elisabeth B. Binder: Max Planck Institute for Psychiatry
Fabian J. Theis: Helmholtz Zentrum München – German Research Center for Environmental Health
Matthias Heinig: Helmholtz Zentrum München – German Research Center for Environmental Health

Nature Communications, 2021, vol. 12, issue 1, 1-18

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26779-7

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DOI: 10.1038/s41467-021-26779-7

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