Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Sep-Oct;9(5):1281-92. doi: 10.1109/TCBB.2012.83.
RNA-Seq is widely used in transcriptome studies, and the detection of differentially expressed genes (DEGs) between two classes of individuals, e.g., cases versus controls, using RNA-Seq is of fundamental importance. Many statistical methods for DEG detection based on RNA-Seq data have been developed and most of them are based on the read counts mapped to individual genes. On the other hand, genes are composed of exons and the distribution of reads for the different exons can be heterogeneous. We hypothesize that the detection accuracy of differentially expressed genes can be increased by analyzing individual exons within a gene and then combining the results of the exons. We therefore developed a novel program, termed CEDER, to accurately detect DEGs by combining the significance of the exons. CEDER first tests for differentially expressed exons yielding a p-value for each, and then gives a score indicating the potential for a gene to be differentially expressed by integrating the p-values of the exons in the gene. We showed that CEDER can significantly increase the accuracy of existing methods for detecting DEGs on two benchmark RNA-Seq data sets and simulated datasets.
RNA-Seq 被广泛应用于转录组研究，使用 RNA-Seq 检测两个类别的个体（例如病例与对照）之间的差异表达基因（DEGs）至关重要。已经开发出许多基于 RNA-Seq 数据的 DEG 检测的统计方法，其中大多数方法都是基于映射到各个基因的读取计数。另一方面，基因由外显子组成，不同外显子的读取分布可能不均匀。我们假设通过分析基因内的各个外显子，然后组合外显子的结果，可以提高差异表达基因的检测准确性。因此，我们开发了一种新程序 CEDER，通过组合外显子的显著性来准确检测 DEGs。CEDER 首先测试差异表达的外显子，为每个外显子生成一个 p 值，然后通过整合基因中外显子的 p 值给出一个指示基因差异表达潜力的分数。我们表明，CEDER 可以显著提高两个基准 RNA-Seq 数据集和模拟数据集上现有方法检测 DEGs 的准确性。
