A framework for robust differential network modular structure discovery from RNA-seq data

Functional gene network analysis, such as gene co-expression network analysis, is useful for detecting disease-associated gene modules. Compared with many gene interaction networks in pathway databases, co-expression networks constructed directly from RNA-seq experiment are context-specific and thus more helpful for detecting differential gene modules under defined conditions. However, existing co-expression network inference approaches for RNA-seq data suffer from high noise and biases due to small sample sizes and many confounding factors. In this paper we proposed a framework for constructing robust, context-specific differential gene co-expression networks consisting of only high confidence edges. To detect disease-associated submodules, we devised a new metric to measure module significance scores. Based on this metric, we developed a gene ontology(GO)-driven module discovery algorithm for identifying disease-associated differential modular structures. Experiments on real RNA-seq data shows this framework works well in detecting biologically meaningful signals.