Methods and approaches in the topology-based analysis of biological pathways

Abstract

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.

Network-based approaches for drug response prediction and targeted therapy development in cancer

“…[56]. These and other topologybased methods for structural analysis of maps are used for retrieving intervention M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 11 combinations in cancer and predicting response to treatment (Table 2 and extensively reviewed in [57]). …”

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

“…To make graph layouts informative and reproducible, several visualization strategies have been proposed and adopted. A frequently used visualization method is the ball-and-stick diagram, where pathway data are presented as networks, with compounds (e.g., metabolites, proteins) as nodes and reactions as edges [23,37,52]. Nodes can be placed hierarchically (a father node with one or more child nodes) or radially as in radial networks or hive plots, which more closely represent the complexity of biological systems [53].…”

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

“…We further observe a recurrent difference between the NPA and most PT methodologies: the goal of the quantification is the determination of the most relevant pathways or networks (compared to the other ones in the collection) to support the biological interpretation. This is achieved by sorting either abstract scores or enrichment p-values [33]. This approach corresponds to the abovementioned competitive Q1 statistic, which suits the discovery rather than the assessment perspective, corresponding to systems toxicology [16,17].…”