DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data
DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data
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DREAMSeq：一种分析 RNA-seq 数据中差异表达基因的改进方法
DOI：
10.3389/fgene.2018.00588
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发表时间：
2018-11-30
影响因子：
3.7
通讯作者：
Tang, Wenqiang
中科院分区：
文献类型：
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作者：
Gao, Zhihua;Zhao, Zhiying;Tang, Wenqiang
RNA测序（RNA-seq）已成为一种广泛应用的技术，用于分析某些生物过程中全局基因表达的变化。一般认为RNA-seq数据具有等分散和过分散的特点；因此，大多数RNA-seq分析方法都是基于负二项模型开发的，能够捕获等分散和过分散的数据。在本研究中，我们报道了除了等分散和过分散之外，RNA-seq数据还显示出一般RNA-seq分析方法无法充分捕获的欠分散特征。基于能够捕获所有数据特征的双泊松模型，我们开发了一种新的RNA-seq分析方法（DREAMSeq）。DREAMSeq与其他五种常用的RNA-seq分析方法使用模拟数据集进行比较，结果表明，DREAMSeq在I型错误率、统计功率、受试者工作特征（ROC）曲线、ROC曲线下面积、精确召回率曲线以及检测差异表达基因数量的能力（特别是在欠分散情况下）方面的性能与其他方法相当或优于其他方法。这些结果通过使用真实的Foxtail数据集进行定量实时聚合酶链反应验证。我们的研究结果表明，DREAMSeq是一种可靠、稳健、强大的RNA-seq数据挖掘新方法。DREAMSeq R包可在http://tanglab.hebtu上获得。edu.cn/tanglab/Home/DREAMSeq。
RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu. edu.cn/tanglab/Home/DREAMSeq.