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Schmid, K. ; Höllbacher, B. ; Cruceanu, C.* ; Böttcher, A. ; Lickert, H. ; Binder, E.B.* ; Theis, F.J. ; Heinig, M.

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

Nat. Commun. 12:6625 (2021)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Differential Expression Analysis; Sample-size; Rna-seq; Power Analysis; Discovery; Signatures; Count
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 12, Heft: 1, Seiten: , Artikelnummer: 6625 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Förderungen Projekt DEAL