Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.
Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.
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DOI：
10.1016/j.cels.2018.06.011
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发表时间：
2018-09-26
期刊：
影响因子：
9.3
通讯作者：
Vallejos CA
中科院分区：
文献类型：
--
作者：
Eling N;Richard AC;Richardson S;Marioni JC;Vallejos CA
同源细胞群体中的细胞间转录变异在组织功能和发育中起着重要作用。单细胞RNA测序可以在转录组范围内表征这种变异性。然而，技术差异以及可变性和平均表达估计之间的混淆阻碍了对细胞群体之间表达可变性的有意义的比较。为了解决这个问题，我们引入了一种分析方法，该方法扩展了基本统计框架，以推导出不受平均值表达式混淆的变异性的残差度量。这包括在没有技术尖峰分子的实验中，对技术噪声进行量化的可靠程序。我们说明了我们的方法如何提供对细胞间表达可变性的动态的生物学洞察，强调免疫细胞中的生物合成机械组件在激活时的同步性。与生物合成机制的统一上调形成对比的是，CD4+T细胞在激活和分化过程中表现出免疫相关和谱系定义基因的异质性上调。单细胞RNA测序数据中均值-变异性相关性的基本量化技术噪声缺失联合差异均值表达和差异变异性检测免疫反应过程中异质性调控基因的检测当从单细胞RNA测序数据中量化表达变异性时，低表达的基因往往比高表达的基因更具变异性。当平均表达发生变化时，这种混杂效应阻碍了两个细胞群体之间稳健的表达可变性测试。Eling等人。引入一种贝叶斯方法，纠正这种混淆效应，并允许联合差分平均和差分变异性检验。这导致了在免疫激活和分化过程中发现了同质性和异质性调节基因。
Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4+ T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation. Correction of the mean-variability dependency in scRNA-seq data using BASiCS Quantification of technical noise when spike-in RNA is missing Joint differential mean expression and differential variability testing Detection of heterogeneously regulated genes during immune responses When quantifying expression variability from single-cell RNA sequencing data, lowly expressed genes tend to be more variable compared to highly expressed genes. This confounding effect hinders robust expression variability testing between two cell populations when mean expression changes. Eling et al. introduce a Bayesian approach that corrects for this confounding effect and allows joint differential mean and differential variability testing. This led to the discovery of homogeneously and heterogeneously regulated genes during immune activation and differentiation.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
64.5
作者：
Goolam M;Scialdone A;Graham SJL;Macaulay IC;Jedrusik A;Hupalowska A;Voet T;Marioni JC;Zernicka-Goetz M
通讯作者：
Zernicka-Goetz M
影响因子：
32.4
作者：
Choi YS;Kageyama R;Eto D;Escobar TC;Johnston RJ;Monticelli L;Lao C;Crotty S
通讯作者：
Crotty S
影响因子：
5.8
作者：
Kapourani, Chantriolnt-Andreas;Sanguinetti, Guido
通讯作者：
Sanguinetti, Guido
影响因子：
64.8
作者：
Chang, Hannah H.;Hemberg, Martin;Huang, Sui
通讯作者：
Huang, Sui