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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) costs have decreased, leading to a surge in available datasets.
• Integrating diverse scRNA-seq datasets is hindered by non-biological variations known as batch effects.
• Evaluating the performance of computational batch correction methods is complex and lacks standardization.

Purpose of the Study:

• To introduce BatchBench, a modular and flexible computational pipeline for comparing batch correction methods in scRNA-seq.
• To provide a systematic framework for assessing the performance and computational demands of different batch correction tools.
• To guide researchers in selecting appropriate batch correction strategies for their specific scRNA-seq data integration needs.

Main Methods:

• Developed BatchBench, a versatile pipeline for evaluating batch effect removal algorithms.
• Applied BatchBench to benchmark eight distinct batch correction methods.
• Utilized a compendium of well-characterized scRNA-seq datasets for comprehensive method assessment.

Main Results:

• Highlighted the methodological distinctions between the eight evaluated batch correction techniques.
• Assessed the performance and computational resource requirements of each method.
• Demonstrated BatchBench's utility in systematically comparing batch correction strategies.

Conclusions:

• BatchBench offers a standardized approach to evaluate and compare batch correction methods for scRNA-seq data.
• The pipeline facilitates informed decision-making for researchers aiming to integrate multiple scRNA-seq datasets.
• BatchBench enables straightforward evaluation of additional datasets and batch correction tools, enhancing reproducibility and data utility.