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Nat Commun. 2019 Oct 11;10(1):4667. doi: 10.1038/s41467-019-12266-7.

A systematic evaluation of single cell RNA-seq analysis pipelines.

Author information

1
Anthropology and Human Genomics, Department of Biology II, Ludwig-Maximilians University, Munich, Germany.
2
Max Planck Institute for Biology of Ageing, Cologne, Germany.
3
Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 65, Stockholm, Sweden.
4
Anthropology and Human Genomics, Department of Biology II, Ludwig-Maximilians University, Munich, Germany. hellmann@bio.lmu.de.

Abstract

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.

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