Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
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DOI：
10.1016/j.cels.2019.03.010
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发表时间：
2019-04-24
期刊：
影响因子：
9.3
通讯作者：
Yosef, Nir
中科院分区：
文献类型：
--
作者：
Cole, Michael B.;Risso, Davide;Yosef, Nir
系统性测量偏差使标准化成为单细胞RNA测序（scRNA-seq）分析的重要步骤。在评估标准化性能的背后可能存在多个相互竞争的考虑因素，其中一些可能是研究特定的。我们开发了“scone”-一个灵活的框架，用于基于全面的数据驱动指标评估绩效。通过图形总结和定量报告，scone总结了权衡，并通过面板性能对大量的标准化方法进行了排名。该方法在开源Bioconductor R软件包Scone中实现。我们发现，对于scRNA-seq数据集的集合，性能最好的标准化方法与独立验证数据的一致性更好。司康饼可在http://bioconductor.org/packages/scone/下载。
Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone''-a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the opensource Bioconductor R software package scone. Weshow that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.