Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Nat Commun. 2020 Feb 7;11(1):774. doi: 10.1038/s41467-020-14482-y.
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.
对于几乎所有单细胞 RNA 测序实验来说,一个基本问题是如何分配有限的测序预算:对少数细胞进行深度测序,还是对多数细胞进行浅度测序?在这里,我们提出了一个数学框架,该框架揭示了,对于估计许多重要的基因特性,最佳分配方案是对每个细胞每个基因进行大约一个读取深度的测序。有趣的是,相应的最优估计器不是广泛使用的插件估计器,而是通过经验贝叶斯方法开发的估计器。