PhD project: Comparing gene-network analysis tools

One of my PhD projects involved evaluating and discussing ‘network-based pathway analysis techniques’, algorithms which find disease associated mechanisms by taking into account the network of interactions between genes and their products. This project was joint work with Dr. Rosemary Braun.

We presented a poster of our work at the Chicago Area SIAM Student Conferencee and the work is currently available on the arXiv.

Scientific summary: Network Methods for Pathway Analysis of Genomic Data

Rapid advances in high-throughput technologies have led to considerable interest in analyzing genome-scale data in the context of biological pathways, with the goal of identifying functional systems that are involved in a given phenotype. In the most common approaches, biological pathways are modeled as simple sets of genes, neglecting the network of interactions comprising the pathway and treating all genes as equally important to the pathway’s function. Recently, a number of new methods have been proposed to integrate pathway topology in the analyses, harnessing existing knowledge and enabling more nuanced models of complex biological systems. However, there is little guidance available to researches choosing between these methods.

In this review, we discuss eight topology-based methods, comparing their methodological approaches and appropriate use cases. In addition, we present the results of the application of these methods to a curated set of ten gene expression profiling studies using a common set of pathway annotations. We report the computational efficiency of the methods and the consistency of the results across methods and studies to help guide users in choosing a method. We also discuss the challenges and future outlook for improved network analysis methodologies.