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PMID: 24056876 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Accounting for technical noise in single-cell RNA-seq experiments.

Nature methods ·Vol. 10 ·No. 11 ·2013-11-00 ·Pages 1093-5

Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG

Abstract

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

MeSH Terms
Sequence Analysis, RNA/methods Single-Cell Analysis
Authors & Affiliations
11 authors, click to expand affiliations / ORCID
Brennecke Philip
1] European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. [2].
Anders Simon
Kim Jong Kyoung
Kołodziejczyk Aleksandra A
Zhang Xiuwei
Proserpio Valentina
Baying Bianka
Benes Vladimir
Teichmann Sarah A
Marioni John C
Heisler Marcus G
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Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7105
Published
2013-11-00
Epub
2013-00-22
Pages
1093-5
Language
English
Region
United States
NLM ID
101215604
Subset
IM
Corrections
ErratumIn
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