Optimal sequencing depth for single-cell RNA-sequencing in Wasserstein space
Jakwang Kim, Sharvaj Kubal, Geoffrey Schiebinger
The Annals of Statistics · 2025 · Volume 53 · Issue 6
Abstract
How many samples should one collect for an empirical distribution to be as close as possible to the true population? This question is not trivial in the context of single-cell RNA-sequencing. With limited sequencing depth, profiling more cells comes at the cost of fewer reads per cell. Therefore, one must strike a balance between the number of cells sampled and the accuracy of each measured gene expression profile. In this paper, we analyze an empirical distribution of cells and obtain upper and lower bounds on the Wasserstein distance to the true population. Our analysis holds for general, nonparametric distributions of cells, and is validated by simulation experiments on a real single-cell dataset.
Keywords
49Q22, 62D99, 62G05, Empirical distribution, nonparametric inference, optimal depth, Optimal transport, RNA sequencing, Wasserstein distance
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