Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network

Gene regulatory network (GRN), which refers to the complex interactions with time delays between TFs and other genes, plays an important role in the working of the cell. Therefore inferring the GRN is crucial to studying diseases related to malfunctioning of the cell. Even with high-throughput technology, time series expression data is still limited compared to the network size, which poses significant challenge to inferring large GRN. Since GRNs are known to be modular, or hierarchically modular, we propose to exploit this by first inferring an initial GRN using CLINDE, then decomposing it into possibly overlapping subnetworks, then re-learning the subnetworks using either CLINDE or DD-lasso, and lastly merging the subnetworks. We have performed extensive experiments on synthetic data to test this strategy on both modular and hierarchically modular networks with 500 and 1000 genes, using either a long time series or several short time series. Results show that the strategy does improve GRN inference with statistical significance. Also, the algorithm is robust to different variance and slight deviation of Gaussianity for the error terms.