RNA-seq has emerged as a powerful technology for the detection of differential gene expression in the transcriptome. The commonly used statistical methods for RNA-seq differential expression analysis were designed for individual genes, which may detect too many irrelevant significantly genes or too few genes to interpret the phenotypic changes. Recently network module-based methods have been proposed as a powerful approach to analyze and interpret expression data in microarray and shotgun proteomics. But the module-based statistical model has not been adequately addressed for RNA-seq data.