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

Abstract

The massive growth of single-cell RNA-sequencing (scRNAseq) and the methods for its analysis still lack sufficient and up-to-date benchmarks that could guide analytical choices. Numerous benchmark studies already exist and cover most of scRNAseq processing and analytical methods but only a few give advice on a comprehensive pipeline. Moreover, current studies often focused on isolated steps of the process and do not address the impact of a tool on both the intermediate and the final steps of the analysis. Here, we present a flexible R framework for pipeline comparison with multi-level evaluation metrics. We apply it to the benchmark of scRNAseq 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. We evaluate the choice of these tools with multi-purpose metrics to assess their ability to reveal cell population structure and lead to efficient clustering. On the basis of our systematic evaluations of analysis pipelines, we make a number of practical recommendations about current analysis choices and for a comprehensive pipeline. The evaluation framework that we developed, pipeComp (https://github.com/plger/pipeComp), has been implemented so as to easily integrate any other step, tool, or evaluation metric allowing extensible benchmarks and easy applications to other fields of research in Bioinformatics, as we demonstrate through a study of the impact of removal of unwanted variation on differential expression analysis.