Malignant progression of normal tissue is typically driven by a complex network of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. In this context, The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating and publicly releasing deeply measured molecular data from thousands of tumor samples. In this work, we investigate two integrative approaches to exploit these rich multi-omic data to provide insight into multi-level gene regulation in cancer. First, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation and germ-line genetic variation. To facilitate an interactive exploration of the results of our transcriptome-wide analyses across 17 different cancers, we provide a freely available, user-friendly, browseable web-based application called EDGE in TCGA (http://ls-shiny-prod.uwm.edu/edge_in_tcga). Second, we develop an exploratory method called padma based on a Multiple Factor Analysis (MFA) to identify and quantify pathway-specific multi-omic deviations between individuals and the overall sampled population. In particular, padma characterizes individuals with aberrant multi-omic profiles for a given pathway of interest and quantifies this deviation with respect to the sampled population using a multi-omic consensus representation. We demonstrate the utility of padma to correlate patient outcomes with complex perturbations to clinically actionable pathways at the genetic, epigenetic, and transcriptomic levels across multiple pathway nodes, which lays the groundwork for enabling personalized treatment strategies that could be tailored to complex patient-specific pathway perturbations.