SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data.

Motivation: RNA-Seq data analysis is commonly biased towards detecting differentially expressed genes and insufficiently conveys the complexity of gene expression changes between biological conditions. This bias arises because discrete models of RNA-Seq count data cannot fully characterize the mean, variance, and skewness of gene expression distribution using independent model parameters. A unified framework that simultaneously tests for differential expression, variability, and skewness is needed to realize the full potential of RNA-Seq data analysis in a systems biology context. Results: We present SIEVE, a statistical methodology that provides the desired unified framework. SIEVE embraces a compositional data analysis framework that transforms discrete RNA-Seq counts to a continuous form with a distribution that is well-fitted by a skew-normal distribution. Simulation results show that SIEVE controls the false discovery rate and probability of Type II error better than existing methods for differential expression analysis. Analysis of the Mayo RNA-Seq dataset for Alzheimer's disease using SIEVE reveals that a gene set with significant expression difference in mean, standard deviation and skewness between the control and the Alzheimer's disease group strongly predicts a subject's disease state. Furthermore, functional enrichment analysis shows that relying solely on differentially expressed genes detects only a segment of a much broader spectrum of biological aspects associated with Alzheimer's disease. The latter aspects can only be revealed using genes that show differential variability and skewness. Thus, SIEVE enables fresh perspectives for understanding the intricate changes in gene expression that occur in complex diseases. Availability: The SIEVE R package and source codes are available at https://github.com/Divo-Lee/SIEVE .