Motivation: High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions. Results: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.
Oxford U.P.
Evolutionary Intelligence

Beisser, D., Brunkhorst, S., Dandekar, T., Klau, G., Dittrich, M., & Müller, T. (2012). Robustness and accuracy of functional modules in integrated network analysis. Bioinformatics, 28(14), 1887–1894. doi:10.1093/bioinformatics/bts265