Integrative analysis of multi-modal correlated imaging-genomics data in glioblastoma


We propose a method to integrate high-dimensional genomics datasets across multiple platforms with multiple correlated imaging outcomes. This framework uses a hierarchical model to integrate biological relationships across platforms to identify genes that associate with correlated outcomes. Our two-stage hierarchical model uses the information shared across the platforms and increases the predictive power to identify the relevant genes. We assess the performance of our proposed method through simulations and apply to data obtained from the Cancer Genome Atlas Glioblastoma Multiforme dataset. Our proposed method discovers multiple copy number and microRNA regulated genes that are related to patients’ imaging outcomes in glioblastoma.

2013 IEEE International Workshop on Genomic Signal Processing and Statistics