View source: R/proposed_steps.R

null_model_refinement_step | R Documentation |

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)

`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 ( |

`Ntargets` |
Number of targets used while building the GBM ( |

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

Raghvendra Mall <rmall@hbku.edu.qa>

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.

`z_score_effect`

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