Background: Gene regulatory interactions are of fundamental importance to various biological functions and\nprocesses. However, only a few previous computational studies have claimed success in revealing genome-wide\nregulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover,\nrecent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100\nor higher.\nResults: Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN)\nstructures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM\nchallenge benchmark data. The highlight of our method is that its superior performance does not degenerate even\nfor a network size on the order of 104, and is thus readily applicable to large-scale complex networks. Such a\nbreakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e.,\nsparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate\nthe application of our algorithm in practice using the time-course gene expression data from a study on human\nrespiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on\ntranscription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that\nthe biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial\ncell infection by IAV.\nConclusions: The proposed algorithm is the first scalable method for large complex network structure identification.\nThe GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose\nwhich gene functions to investigate in a biological event. The algorithm described in this article is implemented in\nMATLAB, and the source code is freely available from https://github.com/Hongyu-Miao/DMI.git.
Loading....