Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench.

ABSTRACT: As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here, we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides

SUBMITTER: Chazarra-Gil R 

PROVIDER: S-EPMC8053088 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature