Improved Functional Enrichment Analysis of Biological Networks using Scalable Modularity Based Clustering

The past decade has seen a rapid growth in the application of mathematical and computational tools for extracting insight from biological networks, and of particular interest here, visualising the community structure within such networks. Clustering approaches have proven useful methods to uncover structural and functional sub-groups from within protein interaction networks. However many commonly used clustering methods for identifying functionally relevant substructures within molecular networks do not perform well with increasing network sizes. We tested the performance of algorithms in terms of their ability to identify functionally relevant sub-clusters within networks of varying size as well as computational performance. Our studies suggest many algorithms perform well on smaller networks but fail to scale with network size. A Spectral based Modularity clustering algorithm, with a fine-tuning step, provided both scalability and improved identification of clusters enriched for functional annotation (e.g. disease) in real proteomic interaction datasets.