pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools

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...

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Bibliographic Details
Published in:	Genome Biology 2020-09, Vol.21 (1), p.227-227, Article 227
Main Authors:	Germain, Pierre-Luc, Sonrel, Anthony, Robinson, Mark D
Format:	Article
Language:	eng
Subjects:	Accuracy Benchmark Clustering Computational Biology - methods Computer applications Data processing Datasets Filtering Humans Method Normalization Pipeline Pipelines Ribonucleic acid RNA Sequence analysis Sequence Analysis, RNA Single-Cell Analysis Single-cell RNA sequencing (scRNAseq) Software
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Summary:	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.
ISSN:	1474-760X 1474-7596 1474-760X