  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.
