Variance component testing for identifying differentially expressed genes in RNA-seq data.
Variance component testing for identifying differentially expressed genes in RNA-seq data.
复制标题
用于识别 RNA-seq 数据中差异表达基因的方差分量测试
作者：
Yang S;Shao F;Duan W;Zhao Y;Chen F
RNA 测序 (RNA-Seq) 能够通过异构体水平定量来测量和比较基因表达。每种亚型的效果差异可能会使聚集亚型的传统方法变得无效。在这里，我们引入了一种基于方差分量的测试，可以联合测试一个基因的多个异构体，以识别差异表达（DE）基因，特别是那些具有差异效应的异构体。我们使用负二项分布对来自 RNA-Seq 的亚型水平表达数据进行建模，并将亚型的基线丰度及其影响视为两个随机项。我们的方法测试了任何亚型没有差异的全局零假设。使用经验和理论方法研究派生得分统计的零分布。仿真结果表明，所提出的集合测试的性能优于传统算法，并且在协变量方差较大时几乎达到最优功效。该方法也适用于分析真实数据。我们的算法作为传统算法的补充，在选择异构体稀疏或相反效果的DE基因方面具有优越性。
RNA sequencing (RNA-Seq) enables the measurement and comparison of gene expression with isoform-level quantification. Differences in the effect of each isoform may make traditional methods, which aggregate isoforms, ineffective. Here, we introduce a variance component-based test that can jointly test multiple isoforms of one gene to identify differentially expressed (DE) genes, especially those with isoforms that have differential effects. We model isoform-level expression data from RNA-Seq using a negative binomial distribution and consider the baseline abundance of isoforms and their effects as two random terms. Our approach tests the global null hypothesis of no difference in any of the isoforms. The null distribution of the derived score statistic is investigated using empirical and theoretical methods. The results of simulations suggest that the performance of the proposed set test is superior to that of traditional algorithms and almost reaches optimal power when the variance of covariates is large. This method is also applied to analyze real data. Our algorithm, as a supplement to traditional algorithms, is superior at selecting DE genes with sparse or opposite effects for isoforms.
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影响因子：
46.9
作者：
Trapnell C;Williams BA;Pertea G;Mortazavi A;Kwan G;van Baren MJ;Salzberg SL;Wold BJ;Pachter L
通讯作者：
Pachter L
使用模拟和真实 miRNA 测序数据对特征选择和分类算法进行系统评估
影响因子：
--
作者：
Yang S;Guo L;Shao F;Zhao Y;Chen F
通讯作者：
Chen F
DOI：
10.1515/1544-6115.1826
发表时间：
2012-01-01
影响因子：
0.9
作者：
Lund, Steven P.;Nettleton, Dan;Smyth, Gordon K.
通讯作者：
Smyth, Gordon K.
影响因子：
12.3
作者：
Kanitz A;Gypas F;Gruber AJ;Gruber AR;Martin G;Zavolan M
通讯作者：
Zavolan M
影响因子：
2.3
作者：
Li J;Tibshirani R
通讯作者：
Tibshirani R