Robust reverse engineering of dynamic gene networks under sample size heterogeneity

Simultaneously reverse engineering a collection of condition-specific gene networks from gene expression microarray data to uncover dynamic mechanisms is a key challenge in systems biology. However, existing methods for this task are very sensitive to variations in the size of the microarray samples across different biological conditions (which we term sample size heterogeneity in network reconstruction), and can potentially produce misleading results that can lead to incorrect biological interpretation. In this work, we develop a more robust framework that addresses this novel problem. Just like microarray measurements across conditions must undergo proper normalization on their magnitudes before entering subsequent analysis, we argue that networks across conditions also need to be "normalized" on their density when they are constructed, and we provide an algorithm that allows such normalization to be facilitated while estimating the networks. We show the quantitative advantages of our approach on synthetic and real data. Our analysis of a hematopoietic stem cell dataset reveals interesting results, some of which are confirmed by previously validated results.