Mentor: Dr. Habtom Ressom, Department of Oncology, Georgetown University Medical Center
Date/Time: May 13, 2019 at 1:30pm
Location: Room 1300, Harris Building
Integrated differential expression and differential network analysis (INDEED) was developed for the identification of biomarkers with significant expression level changes within distinct biomolecule groups. The advantage to INDEED is that it combines differential expression and differential network analysis to facilitate biomarker discovery. The utilization of building sparse differential networks based on partial correlation that integrates differential expression and differential network analysis provides greater insight than using either method alone. The first version of the INDEED R-package which generates a list of data frames containing information such as p-value, node degree and activity score for each biomolecule was incomplete in that it lacked a visual component. This resulted in INDEED not being optimal for making sense of high throughput data in house and ultimately less accessible via using INDEED alone. INDEED has been updated based on user feedback and an interactive visualization tool to facilitate researchers in choosing potential biomarkers has been developed. The update now accounts for many more user errors and avoids certain user data pitfalls that were not accounted for in the previous version of INDEED. The interactive visualization component of the update was tested against a variety of data sets effectively. The final outcome includes an updated and readily available INDEED R-package on GitHub.