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• W2980147288 abstract "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." @default.
• W2980147288 created "2019-10-18" @default.
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• W2980147288 date "2019-10-11" @default.
• W2980147288 modified "2023-10-14" @default.
• W2980147288 title "A systematic evaluation of single cell RNA-seq analysis pipelines" @default.
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• W2980147288 doi "https://doi.org/10.1038/s41467-019-12266-7" @default.
• W2980147288 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6789098" @default.
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• W2980147288 hasPublicationYear "2019" @default.
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