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Maximizing statistical power to detect differentially abundant cell states with scPOST.


ABSTRACT: To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.

SUBMITTER: Millard N 

PROVIDER: S-EPMC8740883 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Maximizing statistical power to detect differentially abundant cell states with scPOST.

Millard Nghia N   Korsunsky Ilya I   Weinand Kathryn K   Fonseka Chamith Y CY   Nathan Aparna A   Kang Joyce B JB   Raychaudhuri Soumya S  

Cell reports methods 20211122 8


To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observe  ...[more]

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