Pathway Enrichment Analysis Based on Estimating the Underlying Network

• Activity Number: 254
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Jing Ma*+ and George Michailidis and Ali Shojaie
• Companies: University of Michigan and University of Michigan and University of Washington
• Keywords: Networks ; High-dimensional data ; Pathway enrichment analysis ; Graphical lasso

Abstract

Enrichment analysis of biological pathways provides biomedical researchers with a systems-level approach for the analysis of changes in cellular activity in response to external stimuli and/or progression of complex diseases. Methods that utilize the pathway topology have been shown to outperform the others. Reliable network information regarding interactions amongst genes can help achieve superior statistical power for testing the significance of pathways. However, such information is still incomplete. There exists only external information about the presence and absence of certain interactions in biological knowledge databases. In this work, we propose an algorithm which incorporates the existing knowledge and estimates the underlying network reliably using high-dimensional data. The performance of the proposed method is illustrated using both simulated data and real data examples.