null_model_refinement_step {RGBM}

Perform the null model refinement step

Description

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.

Usage

null_model_refinement_step(E, A, K,tfs, targets, Ntfs, Ntargets)

Arguments

E

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.

A

Intermediate GRN network in the form of a p-by-p adjacency matrix.

K

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.

tfs

List of names of transcription factors

targets

List of names of target genes

Ntfs

Number of transcription factors used while building the GBM (GBM) model.

Ntargets

Number of targets used while building the GBM (GBM) model.

Value

Returns a refined adjacency matrix A in the form of a Ntfs-by-Ntargets matrix.

Author(s)

Raghvendra Mall <[e-mail]>

References

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.

See Also

z_score_effect