Guideline for comparing functional enrichment of biological network modular structures

The use of networks to analyze biological data, such as large gene or protein expression datasets, is on the rise. Often, there is an interest of identifying modules (or communities) of biological molecules that may be associated to known functions. This functional modularity analyses usually revolve around a workflow that combines 1) a method for network reconstruction from biological data, 2) a community or clustering algorithm on a network, and 3) an enrichment analysis to associate modules to known biological categories. With this, it is possible to identify sets of functions associated to modules in networks of distinct biological conditions, allowing for the comparison of such different phenotypes. Currently there is no set of recommendations for such analyses, which can lead to problems in assessing these results for a given biological context. Furthermore, without properly identifying the methodological scopes and limitations at each stage for a given functional modularity analysis, it is not immediately possible to compare the biological implications of analyses in different phenotypes. In this work, critical points in a functional modularity analysis for biological networks are identified, and methods are proposed for assessing the topological and biological results of functional modularity analyses in biological networks, and to calculate topological and functional similarity between comparable phenotypes. These methods are demonstrated on biological networks artificially constructed from known biological pathways.