Comparative analysis of single-cell RNA sequencing methods
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- dc.contributor.author Ziegenhain, Christophca
- dc.contributor.author Vieth, Beate I.H.ca
- dc.contributor.author Parekh, Swatica
- dc.contributor.author Reinius, Björnca
- dc.contributor.author Guillaumet-Adkins, Amyca
- dc.contributor.author Smets, Marthaca
- dc.contributor.author Leonhardt, Heinrichca
- dc.contributor.author Heyn, Holgerca
- dc.contributor.author Hellmann, Inesca
- dc.contributor.author Enard, Wolfgangca
- dc.date.accessioned 2018-06-19T07:54:00Z
- dc.date.available 2018-06-19T07:54:00Z
- dc.date.issued 2017
- dc.description.abstract Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
- dc.format.mimetype application/pdf
- dc.identifier.citation Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M et al. Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017 Feb 16;65(4):631-43.e4. DOI: 10.1016/j.molcel.2017.01.023
- dc.identifier.doi http://dx.doi.org/10.1016/j.molcel.2017.01.023
- dc.identifier.issn 1097-2765
- dc.identifier.uri http://hdl.handle.net/10230/34928
- dc.language.iso eng
- dc.publisher Elsevierca
- dc.relation.ispartof Molecular Cell. 2017 Feb 16;65(4):631-43.e4
- dc.rights © Elsevier This is the published version of an article http://dx.doi.org/10.1016/j.molcel.2017.01.023 that appeared in the journal Molecular Cell. It is published in an Open Archive under an Elsevier user license. Details of this licence are available here: https://www.elsevier.com/about/our-business/policies/open-access-licenses/elsevier-user-license
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Cost-effectiveness
- dc.subject.keyword Method comparison
- dc.subject.keyword Power analysis
- dc.subject.keyword Single-cell rna-seq
- dc.subject.keyword Transcriptomics
- dc.title Comparative analysis of single-cell RNA sequencing methodsca
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion