scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

scRNASeq data is revolutionizing our understanding of biological systems, but is still expensive to generate. Here, the authors present a statistical framework that facilitates informed multi-sample experimental design to reduce unnecessary costs and maximize the utility of the generated data.

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Bibliographic Details
Main Authors:	Katharina T. Schmid, Barbara Höllbacher, Cristiana Cruceanu, Anika Böttcher, Heiko Lickert, Elisabeth B. Binder, Fabian J. Theis, Matthias Heinig
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Science Q
Online Access:	https://doaj.org/article/244a1e2437984f2681930cdb0b3790c0
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id	oai:doaj.org-article:244a1e2437984f2681930cdb0b3790c0
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spelling	oai:doaj.org-article:244a1e2437984f2681930cdb0b3790c02021-11-21T12:36:02ZscPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies10.1038/s41467-021-26779-72041-1723https://doaj.org/article/244a1e2437984f2681930cdb0b3790c02021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26779-7https://doaj.org/toc/2041-1723scRNASeq data is revolutionizing our understanding of biological systems, but is still expensive to generate. Here, the authors present a statistical framework that facilitates informed multi-sample experimental design to reduce unnecessary costs and maximize the utility of the generated data.Katharina T. SchmidBarbara HöllbacherCristiana CruceanuAnika BöttcherHeiko LickertElisabeth B. BinderFabian J. TheisMatthias HeinigNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Katharina T. Schmid Barbara Höllbacher Cristiana Cruceanu Anika Böttcher Heiko Lickert Elisabeth B. Binder Fabian J. Theis Matthias Heinig scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
description	scRNASeq data is revolutionizing our understanding of biological systems, but is still expensive to generate. Here, the authors present a statistical framework that facilitates informed multi-sample experimental design to reduce unnecessary costs and maximize the utility of the generated data.
format	article
author	Katharina T. Schmid Barbara Höllbacher Cristiana Cruceanu Anika Böttcher Heiko Lickert Elisabeth B. Binder Fabian J. Theis Matthias Heinig
author_facet	Katharina T. Schmid Barbara Höllbacher Cristiana Cruceanu Anika Böttcher Heiko Lickert Elisabeth B. Binder Fabian J. Theis Matthias Heinig
author_sort	Katharina T. Schmid
title	scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
title_short	scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
title_full	scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
title_fullStr	scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
title_full_unstemmed	scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
title_sort	scpower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/244a1e2437984f2681930cdb0b3790c0
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scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

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