Determining sequencing depth in a single-cell RNA-seq experiment

Abstract

An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.

Abstract

For single-cell RNA-seq experiments the sequencing budget is limited, and how it should be optimally allocated to maximize information is not clear. Here the authors develop a mathematical framework to show that, for estimating many gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene.