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学術論文

A systematic evaluation of single cell RNA-seq analysis pipelines

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Parekh,  S.
Tessarz – Chromatin and Ageing, Max Planck Research Groups, Max Planck Institute for Biology of Ageing, Max Planck Society;

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https://www.nature.com/articles/s41467-019-12266-7
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引用

Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019). A systematic evaluation of single cell RNA-seq analysis pipelines. Nat Commun, 10(1), 4667. doi:10.1038/s41467-019-12266-7.


引用: https://hdl.handle.net/21.11116/0000-000B-2BA3-D
要旨
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.