Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.
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DOI：
10.1186/gb-2013-14-9-r95
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发表时间：
2013
期刊：
影响因子：
12.3
通讯作者：
Betel D
中科院分区：
文献类型：
--
作者：
Rapaport F;Khanin R;Liang Y;Pirun M;Krek A;Zumbo P;Mason CE;Socci ND;Betel D
已经开发了大量的计算方法来分析RNA-seq数据中的差异基因表达。我们使用SEQC基准数据集和ENCODE数据对常用方法进行了全面评估。我们考虑了一些关键特征，包括归一化、差异表达检测的准确性以及当一个条件没有可检测的表达时的差异表达分析。我们发现这些方法之间存在显着差异，但注意到基于阵列的方法适用于RNA-seq数据，与为RNA-seq设计的方法相比，其性能更好。我们的结果表明，增加重复样品的数量显着提高检测能力超过增加测序深度。
A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.