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.

比较不同技术和不同实验室生成的不同单细胞 RNA 测序 (scRNA-seq) 数据集仍然是一项重大挑战。在这里，我们解决了选择算法的指导需求，从而对通过不同平台获取的各种数据类型进行准确的生物学解释。使用单独或混合捕获的两个充分表征的细胞参考样本（乳腺癌细胞和 B 细胞），我们比较了不同的 scRNA-seq 平台以及多个中心的几种预处理、标准化和批量效应校正方法。尽管预处理和标准化导致基因检测和细胞分类的变异性，但批次效应校正是迄今为止正确分类细胞的最重要因素。此外，scRNA-seq 数据集特征（例如样本和细胞异质性以及使用的平台）对于确定最佳生物信息学方法至关重要。然而，当应用适当的生物信息学方法时，跨中心和平台的重现性很高。我们的研究结果为设计 scRNA-seq 研究时优化平台和软件选择提供了实用指导。