We used this function for refining the edge-weights in an inferred GRN (A) by utilizing matrix (S2) obtained from null-mutant zscore effect (z_score_effect) as shown in Slawek J, Arodz T i.e. A = A x S2.
null_model_refinement_step(E, A, K,tfs, targets, Ntfs, Ntargets)N-by-p expression matrix. Columns correspond to genes, rows correspond to experiments. E is expected to be already normalized using standard methods, for example RMA. Colnames of E is the set of all genes.
Intermediate GRN network in the form of a p-by-p adjacency matrix.
N-by-p initial perturbation matrix. It directly corresponds to E matrix, e.g. if K[i,j] is equal to 1, it means that gene j was knocked-out in experiment i. Single gene knock-out experiments are rows of K with only one value 1. Colnames of K is set to be the set of all genes. By default it's a matrix of zeros of the same size as E, e.g. unknown initial perturbation state of genes.
List of names of transcription factors
List of names of target genes
Number of transcription factors used while building the GBM (GBM) model.
Number of targets used while building the GBM (GBM) model.
Returns a refined adjacency matrix A in the form of a Ntfs-by-Ntargets matrix.
Slawek J, Arodz T. ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC systems biology. 2013 Oct 22;7(1):1.