A practical solution to pseudoreplication bias in single-cell studies

Kip D. Zimmerman (), Mark A. Espeland and Carl D. Langefeld ()
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Kip D. Zimmerman: Wake Forest School of Medicine
Mark A. Espeland: Wake Forest School of Medicine
Carl D. Langefeld: Wake Forest School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Cells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

Date: 2021
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DOI: 10.1038/s41467-021-21038-1

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