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 modular descriptions that provide networkpictures at different resolution levels. We aimed to analyze how these 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 procedures yielded different results in terms of cluster sizes, their biological congruencyand displayed meso-scale connectivity patterns. We specifically studied the case of aging related proteins, and showed that only the high-resolution modular description , achieved by infomap, could unveil statistically significant associations between them and inter/intra modular connectivity schemes. In particular, we found that aging related proteins were more likely to reside in the interface of network modules, possibly linking distinct biological processes. Besides reporting novel biological insights that could be gained from the discovered associations, our results warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies  .
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 .
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dissimilar modular decompositions of a given network. In this contribution, we
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cluster sizes, their biological congruencyand displayed
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We specifically studied the case
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inter/intra modular connectivity schemes. In particular, we found that aging related proteins were more likely to reside in the interface of network modules, possibly linking distinct biological processes.
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results
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. 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