Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes.

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.