Powerful differential expression analysis incorporating network topology for next-generation sequencing data

Abstract

Motivation: RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data. Results: We propose a three-state Markov Random Field (MRF) method that utilizes known biological pathways and interaction to improve sensitivity and specificity and therefore reducing false discovery rates (FDRs) when detecting di..