规范化(社会学)
预处理器
标杆管理
计算机科学
计算生物学
数据挖掘
软件
生物
人工智能
人类学
社会学
业务
营销
程序设计语言
作者
Wanqiu Chen,Yongmei Zhao,Xin Chen,Zhaowei Yang,Xiaojiang Xu,Yingtao Bi,Vicky Chen,Jing Li,Hannah Choi,Ben Ernest,Bao Tran,Monika Mehta,Parimal Kumar,Andrew Farmer,Alain Mir,Urvashi Mehra,Jian‐Liang Li,Malcolm Moos,Wenming Xiao,Charles Wang
标识
DOI:10.1038/s41587-020-00748-9
摘要
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. 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.
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