It's All About the Residuals: Estimating Gene Networks by Leveraging Residual Structure in Multi-Tissue Transcriptomic Data

• Activity Number: 455 - Oncoimmunology Gene Networks: Parallel Data Studies of Multi-Tissue Network Analysis
• Type: Topic Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Matthew McCall*
• Companies: University of Rochester Medical Center
• Keywords: gene network ; cancer ; multi-tissue ; gene expression ; immune response

Abstract

Genes do not act in isolation; rather they interact in complex networks that control cellular function. Statistical models of gene regulatory networks are necessary to investigate cellular mechanisms and to predict response to interventions. In particular, these models have the potential to improve estimation of cancer prognosis and to predict response to treatment. Current methods typically focus on the gene regulatory network of the malignant cells; however, the immune compartment has been shown to play a critical role in cancer progression and prognosis. Just as genes do not act in isolation, neither do cells. Multi-tissue transcriptomic profiling of cancer patients can be used to provide a systems view of cancer. While many alterations in tumor gene expression can be explained by clinical variables, other changes are due to changes in immune cells. After accounting for these sources of variation, the residual variability in tumor gene expression can be used to estimate the regulatory relationships present with malignant cells and how these relationships depend upon the immune compartment.?