Integrative Sparse Bayesian Analysis of High-dimensional Multi-platform Genomic Data in Glioblastoma

While individual studies have demonstrated that mRNA expressions are affected by copy number aberrations and microRNAs, their integrative analysis has largely been ignored. In this article, we use recently developed high-dimensional regression techniques to perform the integrative analysis of such data in the context of Glioblastoma Multiforme (GBM). It is revealed that copy numbers are more potent regulators of mRNA levels than microRNAs. We also infer the mRNA expression network after adjusting the effect of microR-NAs and copy numbers. Our association analysis demonstrates the expression levels of the genes IRS1 and GRB2 are strongly associated with the underlying variation in copy numbers, but we fail to detect significant associations with microRNA levels.