Step 2: Data preprocessing methods

Missing value

impute
imputeKNN

Step 3: Choose Method for Generate Gene regulatory network

C3NET C3NET method allows inferring gene regulatory networks with direct physical interactions from microarray expression data. It consists of two main steps. The first step is the same as the relevance networks(RELNET), where all the non-significant mutual information values in the matrix are eliminated if statistically not significant. The second step of C3NET keeps all maximum valued mutual information values for each row in the matrix and sets the rest of the elements in the matrix zero (the diagonal of the matrix is ignored). The output is normally symmetric matrix but if the argument sym is set to FALSE the the output becomes non-symmetric.
RTN RTN is for reconstruction ana analysis of transcriptional networks(TN) using mutual information. It computes the mutual information between annotated transcription factors and all potential targets using gene expression data.
BioNet This tool provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. Please refer to BioNet for more information.