Mining the modular structure of protein interaction networks

TL;DR

This paper compares different clustering algorithms for protein interaction networks to understand how their modular decompositions influence biological insights, highlighting the importance of high-resolution methods like infomap.

Contribution

It analyzes the impact of different clustering algorithms on biological interpretation and introduces the significance of high-resolution modular descriptions in revealing meaningful biological associations.

Findings

Infomap uncovers significant associations in aging-related proteins.

Different clustering methods yield varying biological insights.

Sub-optimal partitions can still be valuable for meso-scale analysis.

Abstract

Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed at what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. As a case study we considered a set of aging related proteins, and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter-intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.