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

Martin Jinye Zhang, Vasilis Ntranos and David Tse ()
Additional contact information
Martin Jinye Zhang: Stanford University
Vasilis Ntranos: Stanford University
David Tse: Stanford University

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14482-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14482-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-14482-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().