A Multi-center Study Benchmarking Single-cell RNA Sequencing Technologies Using Reference Samples

Researchers continue to face major challenges when comparing diverse single-cell RNA sequencing (scRNA-seq) data sets, because these data often are generated by different technologies from a variety of laboratories.

In this webinar, Dr. Charles Wang will address the need for guidelines to help choose algorithms for more accurate biological interpretations of varied data types acquired by different platforms.

Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, Dr. Wang compared different scRNA-seq platforms and several methods (preprocessing, normalization, and batch-effect correction) 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 data set characteristics (e.g., sample and cellular heterogeneity, the platform used, etc.) were critical in determining the optimal bioinformatics method. However, reproducibility across centers and platforms was high when appropriate bioinformatics methods were applied.

These findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.