Multivariate Covariance using Principal Component Analysis for Reconstruction of Bidirected Gene Regulatory Networks

Gene Regulatory Networks (GRNs) visualize the dynamic behavior of genetic components and their complex interactions. Reconstructing GRNs from gene expression data is in practice using multiple approaches. These approaches result in symmetrical and diagonal gene interaction matrices. However, they cannot predict all kinds of regulatory network motifs between gene pairs, such as activation, inhibition and self regulations. The proposed approach applies multivariate covariances and Principal Component Analysis (PCA) to identify asymmetric and non-diagonal gene interactions. The proposed approach generated bidirected GRNs holding all kinds of regulatory interactions. Moreover, the identified gene regulatory interactions have been verified from the literature.