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 users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.
Highlights
Even though the samples were collected, processed and annotated independently, several comparisons have shown that batch effects can be overcome (19,30)
Visualization of the uncorrected data using Uniform Manifold Approximation and Projection (UMAP) reveals a clear separation of the major cell types across batches (Fig 2a)
We have developed BatchBench, a customizable pipeline for comparing scRNA-seq batch correction methods
Summary
There are currently a plethora of different protocols and experimental platforms available (17,18). It is well known that these and other technical differences can impact the observed expression values, and if not properly accounted for they could be confounded with biological signals [19]. Such differences arising due to non-biological factors are commonly known as batch effects. With appropriate experimental design it is possible to remove a portion of the batch effects computationally, and recently there has been a large degree of interest in developing such methods for scRNA-seq.
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