University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
Networks have become an important tool for the analysis of complex systems across many different disciplines including computer science, biology, chemistry, social sciences, and importantly, cancer medicine. Networks in the real world typically exhibit many forms of higher order organization. The subfield of networks analysis known as community detection aims to provide tools for discovering and interpreting the global structure of a networks-based on the connectivity patterns of its edges. In this thesis, we provide an overview of the methods for community detection in networks with an emphasis on modularity-based approaches. We discuss several caveats and drawbacks of currently available methods. We also review the success that network analyses have had in interpreting large scale 'omics' data in the context of cancer biology. In the second and third chapters, we present CHAMP and multimodbp, two useful community detection tools that seek to overcome several of the deficiencies in modularity-based community detection. In the final chapter, we develop a networks-based significance test for addressing an important question in the field of oncology: are mutations in DNA damage repair genes associated with elevated levels of tumor mutational burden. We apply the tools of network analysis to this question and showcase how this approach yields new insight into the structure of the problem, revealing what we call the TMB Paradox. We close by demonstrating the clinical utility of our findings in predicting patient response to novel immunotherapies.Doctor of Philosoph
