Frequency domain discovery of gene regulatory networks

Discovery of gene regulatory network (GRN) from gene expression data gives an insight into tumor developments and underlying structures. Granger causality (GC) is considered as a powerful tool to detect the interactions between elements of a network. Among the various methods suggested for GC, we use Pairwise GC (PGC), Kernel GC (KGC) and Correntropy method. Also GC is defined in two domains. In time domain, GC cannot correctly determine how strongly one time series influences the other when there is directional causality between them, this limitation necessitates an alternative method. In this regard, GC in frequency domain is being applied as a solution. In this paper, first, we conduct a frequency domain analysis on these methods theoretically. We then evaluate the performance of PGC in both domains by applying real HeLa dataset with three experiments and compare it with previous work. Finally, we apply all methods to both synthetic data and a 94-gene HeLa data to illustrate the discovered networks. We show that frequency domain has better performance in discovery of relations at all experiments.