A systematic evaluation of single cell RNA-seq analysis pipelines
Abstract
<jats:title>Abstract</jats:title><jats:p>The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.</jats:p>
Journal
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- Nature Communications
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Nature Communications 10 (1), 4667-, 2019-10-11
Springer Science and Business Media LLC
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Details 詳細情報について
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- CRID
- 1361418520219832448
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- ISSN
- 20411723
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- Data Source
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- Crossref