Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously

Abstract

BackgroundUsing next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive; most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition.ResultsWe designed an empirical Bayes method and a pooled permutation test to simultaneously consider the change in mean and dispersion across treatment groups. We further computed confidence intervals based on Bayes estimates to identify differentially expressed genes that are unique to each disease sample as well as those that are common across all disease samples. We illustrated our method by applying it to gene expression data from a large offspring syndrome experiment, which motivated this study. We compared our method to competing approaches through simulation studies that mimicked the real datasets to demonstrate the effectiveness of our proposed method.ConclusionsWe will show that, compared to popular methods that only aim to find the difference in the mean, our method can capture greater variation in the disease group to effectively identify differentially expressed genes for syndromes.

Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skeletal muscle to nutrient supply

“…Moreover, gene expression variance (GEV), also referred as gene dispersion, has been often overlooked, being considered just as experimental noise without any biological significance [9]. Few methods have been explicitly designed for modeling GEV across samples in RNA-Seq experiments [10,11], despite the fact that changes in gene expression in response to a specific stimulus might have a biologically meaningful individual component that should not be confounded with experimental noise. Indeed, metabolic responses to nutritional factors are often driven by complex signaling pathways and geneto-gene interactions that are not necessarily identical across the whole cohort of analyzed biological replicates, adding an intrinsic source of variation in gene expression patterns that is often ignored or modeled as a constant variable [11].…”