Statistical and Machine Learning Approaches for Network Analysis

4.4 Discussion and Concluding Remarks

In the present study, we observed that the consideration of coexpression dynamics enables a strong localization power in PIN that reinforces module detection and characterizes phase-specific modules. We also found that the analysis centered on phases annotated in cores and communities reveals a certain convergence between the various PIN. Even if cores do not show the strong G1 characterization in cePIN that is observed with communities, the module annotation quality relatively to the matched complexes depends necessarily on the different resolutions allowed by the methods. Thus, the main driver of modularization is resolution-dependent, as expected.

The proposed approach of PIN fragmentation offers the possibility of looking at a compilation of PIN selected according to various criteria, for instance cell cycle specificity. An advantage is that comparative evaluations with regard to both general topological features and modularity can refer to multiple PIN referred to a common source. Thus, our study has referred to what we defined an “affine” PIN list, which opened the possibility to explore PIN dynamical aspects. Due to the consideration of time-course experiments and their recorded gene expression peak signatures, we could center the rest of the analysis on modularity with reference to the cell cycle role in determining the “interaction driver,” and the integrated gene measurements role in determining the “expression driver.”

Modularization ...