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

Nat Commun. 2020 Feb 7;11(1):774. doi: 10.1038/s41467-020-14482-y.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computational Biology / methods*
  • Computational Biology / statistics & numerical data
  • Gene Expression
  • Gene Regulatory Networks
  • In Situ Hybridization, Fluorescence
  • Models, Theoretical
  • Reproducibility of Results
  • S100 Calcium-Binding Protein A4 / genetics
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / statistics & numerical data*
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / statistics & numerical data*

Substances

  • S100 Calcium-Binding Protein A4