The discovery of disease-specific modules is an important problem for understanding the disease behavior. A successful method to address this problem is the integration of gene expression data with the protein-protein interaction (PPI) network. Many tools have been developed to efficiently perform this integration. However, they focus only on the genes existing in the PPI network; totally neglecting other genes that we do not have yet information regarding their interaction. In addition, they only make use of gene expression data which does not give the true picture about the actual protein expression levels. In fact, the cell uses different mechanisms, such as microRNAs, to post-transcriptionally regulate the proteins without affecting the corresponding genes expressions. The unprecedented amount of publicly available disease-related data encourages the development of new methodologies for a further understanding the disease behavior. In this work, we propose a novel workflow MICA, which, to the best of our knowledge, is the first study integrating miRNA, mRNA, and PPI network information to successfully return disease-specific gene modules. The novelty of the workflow lies in many directions, including the adjustment of mRNA expression with microRNA to better highlight indirect dependencies between the different genes. We applied MICA on microRNA-Seq and mRNA-Seq data sets of 699 invasive ductal carcinoma samples and 150 invasive lobular carcinoma samples from the Cancer Genome Atlas Project (TCGA). The returned MICA gene modules unravel new and interesting dependencies between the different genes and miRNAs.
Technical report for the MICA workflow, a shorter version is submitted to BCB15 conference: mica.pdf
Latest release: mica-1-0