A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

A comprehensive comparison of 20 single-cell RNA-seq datasets derived from the two cell lines analyzed using six preprocessing pipelines, eight normalization methods and seven batch-correction algorithms derived from four different sequencing platforms at different centers. Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Automated summary

The study compared 20 single-cell RNA-seq datasets and investigated the effects of preprocessing, normalization, and batch-effect correction methods on gene detection and cell classification. It was found that batch-effect correction was the most crucial factor for correct cell classification, and dataset characteristics played a critical role in determining the optimal bioinformatic method.