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

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 proper...

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Autores principales: Zhang, Martin Jinye, Ntranos, Vasilis, Tse, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005864/
https://www.ncbi.nlm.nih.gov/pubmed/32034137
http://dx.doi.org/10.1038/s41467-020-14482-y
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author Zhang, Martin Jinye
Ntranos, Vasilis
Tse, David
author_facet Zhang, Martin Jinye
Ntranos, Vasilis
Tse, David
author_sort Zhang, Martin Jinye
collection PubMed
description 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.
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spelling pubmed-70058642020-02-10 Determining sequencing depth in a single-cell RNA-seq experiment Zhang, Martin Jinye Ntranos, Vasilis Tse, David Nat Commun Article 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. Nature Publishing Group UK 2020-02-07 /pmc/articles/PMC7005864/ /pubmed/32034137 http://dx.doi.org/10.1038/s41467-020-14482-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhang, Martin Jinye
Ntranos, Vasilis
Tse, David
Determining sequencing depth in a single-cell RNA-seq experiment
title Determining sequencing depth in a single-cell RNA-seq experiment
title_full Determining sequencing depth in a single-cell RNA-seq experiment
title_fullStr Determining sequencing depth in a single-cell RNA-seq experiment
title_full_unstemmed Determining sequencing depth in a single-cell RNA-seq experiment
title_short Determining sequencing depth in a single-cell RNA-seq experiment
title_sort determining sequencing depth in a single-cell rna-seq experiment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005864/
https://www.ncbi.nlm.nih.gov/pubmed/32034137
http://dx.doi.org/10.1038/s41467-020-14482-y
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