Network Methods for Pathway Analysis of Genomic Data

TL;DR

This paper reviews topology-based network methods for pathway analysis in genomic data, comparing their approaches, efficiency, and consistency across studies to guide researchers in method selection.

Contribution

It provides a comprehensive comparison of eight network-based pathway analysis methods and evaluates their performance on real gene expression data.

Findings

Methods vary in computational efficiency

Results are consistent across different studies

Guidance provided for method selection

Abstract

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.