A review on moderated-t methods for differential expression detection

Abstract

With the advancement of high-throughput technology, identifying differential expression has become an essential task in multiple domains of biomedical research, such as transcriptome, proteome, metabolome. A wide variety of computational methods and statistical approaches were developed for detecting differential expression. Most of these methods were applicable to modeling expression level of the entire set of features simultaneously. In this article, we provide a review emphasizing on moderated-t methods published in last two decades. We compared similarities and differences between them, and also discussed their limitations in applications.