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bioRxiv · 10.1101/2020.01.15.906248

Pseudoreplication bias in single-cell studies; a practical solution

Zimmerman, K. D.·Espeland, M. A.·Langefeld, C. D.
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Abstract

Cells from the same individual share a common genetic and environmental background and are not 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 overly conservative and underpowered relative to mixed models. We propose applying two-part hurdle 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.

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Zimmerman, K. D., Espeland, M. A., Langefeld, C. D.. 2020-01-15. Pseudoreplication bias in single-cell studies; a practical solution. https://doi.org/10.1101/2020.01.15.906248

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