Use of Structural Properties of Underlying Graphs in Pathway Enrichment Analysis of Genomic Data

Common methods for the functional inference of genomic data, such as Gene Sent Enrichment Analysis (GSEA) and Over Representation Analysis (ORA), often discard the interactions between the biomolecular entities. Recent studies have explored this issue through a variety of techniques and show that using evidence from the interactions produces a more relevant and insightful inference. In this article, we introduce a method, referred to as Causal Disturbance (Cdist), for enrichment analysis. Cdist utilizes the underlying graph of pathways in combination with experimental data to detect the pathway dysregulations. To test our methodology, we utilized a public microarray data from colorectal cancer. We show that Cdist identifies the dysregulated pathways of colorectal cancer that are not detectable by other conventional methods. Some of the detected pathways by Cdist, such as apoptosis and Ras signaling, are critical for their roles in cancer. We conclude that our method facilitates a more informative inference of the disease data by incorporating the topological features of the pathway graphs. Using these features will help to detect the pathway dysregulations that are not observable through conventional models.