RNA-Seq is a developing technology for generating gene expression data by directly sequencing mRNA molecules in a sample. RNA-Seq data consist of counts of reads recorded to a particular gene that are often used to identify differentially expressed (DE) genes. A common statistical method used to analyze RNA-Seq data is Significance Analysis of Microarray with emphasis on RNA-Seq data (SAMseq). SAMseq is a nonparametric method that uses a resampling technique to account for differences in sequencing depths when identifying DE genes. We propose a modification of this method that takes into account asymmetry in the distribution of the effect sizes by taking into account the sign of the test statistics. Through simulation studies, we showthat the proposed method, comparedwith the traditional SAMseqmethod and other existing methods provides better power for identifying truly DE genes or more sufficiently controls FDR in most settings where asymmetry is present. We illustrate the use of the proposed method by analyzing an RNA-Seq data set containing C57BL/6J (B6) and DBA/2J (D2) mouse strains samples.

中文翻译：

---

在识别差异表达基因时修改 SAMseq 以解决效应大小分布的不对称性

RNA-Seq 是一种通过直接对样本中的 mRNA 分子进行测序来生成基因表达数据的开发技术。RNA-Seq 数据由记录到特定基因的读数计数组成，这些基因通常用于识别差异表达 (DE) 基因。用于分析 RNA-Seq 数据的常用统计方法是微阵列的显着性分析，重点是 RNA-Seq 数据 (SAMseq)。SAMseq 是一种非参数方法，它在识别 DE 基因时使用重采样技术来解释测序深度的差异。我们建议对该方法进行修改，通过考虑检验统计量的符号来考虑效应大小分布的不对称性。通过仿真研究，我们表明所提出的方法，与传统的 SAMseq 方法和其他现有方法相比，在大多数存在不对称性的情况下，它为识别真正的 DE 基因或更充分地控制 FDR 提供了更好的能力。我们通过分析包含 C57BL/6J (B6) 和 DBA/2J (D2) 小鼠品系样本的 RNA-Seq 数据集来说明该方法的使用。