Robust inference of gene regulatory networks from multiple microarray datasets

Multiple time-course microarray datasets with the same underlying gene network are collected from different experiments. The inference of gene regulatory networks (GRNs) can be improved by integrating these datasets. Microarray data may be contaminated with large errors or outliers, which may affect the inference results. A novel method, Huber group LASSO, is proposed to reconstruct the GRNs from multiple datasets as well as taking the robustness into account. To solve the optimization problem involved in the proposed method, an efficient algorithm which combines the ideas of auxiliary function minimization and block coordinate descent is developed. Simulations and real data applications demonstrate the effectiveness of our method. Results show that the proposed method outperforms the group LASSO method and is able to reconstruct reasonably good GRNs from multiple datasets even the number of genes exceeds the number of observations.