Latest revision as of 14:47, 12 February 2021

Abstract

We present pipeComp ( https://github.com/plger/pipeComp ), a flexible R framework for pipeline comparison handling interactions between analysis steps and relying on multi-level evaluation metrics. We apply it to the benchmark of single-cell RNA-sequencing analysis pipelines using simulated and real datasets with known cell identities, covering common methods of filtering, doublet detection, normalization, feature selection, denoising, dimensionality reduction, and clustering. pipeComp can easily integrate any other step, tool, or evaluation metric, allowing extensible benchmarks and easy applications to other fields, as we demonstrate through a study of the impact of removal of unwanted variation on differential expression analysis.

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• http://dx.doi.org/10.3929/ethz-b-000439493 under the license http://creativecommons.org/licenses/by/4.0/

• http://europepmc.org/articles/PMC7465801 under the license https://creativecommons.org/licenses/by/4.0/legalcode

• http://hdl.handle.net/20.500.11850/439493

• https://genomebiology.biomedcentral.com/track/pdf/10.1186/s13059-020-02136-7 under the license http://creativecommons.org/licenses/by/4.0/

• http://link.springer.com/article/10.1186/s13059-020-02136-7,

https://doaj.org/toc/1474-760X under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s13059-020-02136-7.pdf,

http://link.springer.com/article/10.1186/s13059-020-02136-7/fulltext.html,

http://dx.doi.org/10.1186/s13059-020-02136-7

• https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02136-7,

https://www.research-collection.ethz.ch/handle/20.500.11850/439493,

https://europepmc.org/article/PPR/PPR111323,

https://academic.microsoft.com/#/detail/3081957388 under the license http://creativecommons.org/licenses/by/4.0/

DOIS: 10.1186/s13059-020-02136-7 10.3929/ethz-b-000439493