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Significant advances in biotechnology have allowed for simultaneousmeasurement of molecular data points across multiple genomic and transcriptomiclevels from a single tumor/cancer sample. This has motivated systematicapproaches to integrate multi-dimensional structured datasets since cancerdevelopment and progression is driven by numerous co-ordinated molecularalterations and the interactions between them. We propose a novel two-stepBayesian approach that combines a variable selection framework with integrativestructure learning between multiple sources of data. The structure learning inthe first step is accomplished through novel joint graphical models forheterogeneous (mixed scale) data allowing for flexible incorporation of priorknowledge. This structure learning subsequently informs the variable selectionin the second step to identify groups of molecular features within and acrossplatforms associated with outcomes of cancer progression. The variableselection strategy adjusts for collinearity and multiplicity, and also hastheoretical justifications. We evaluate our methods through simulations andapply them to a motivating genomic (DNA copy number and methylation) andtranscriptomic (mRNA expression) data for assessing important markersassociated with Glioblastoma progression.

Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics