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

Genome Biol. 2020 Sep 1;21(1):227. doi: 10.1186/s13059-020-02136-7.


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.

Keywords: Benchmark; Clustering; Filtering; Normalization; Pipeline; Single-cell RNA sequencing (scRNAseq).

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Humans
  • Sequence Analysis, RNA*
  • Single-Cell Analysis*
  • Software*