second_GBM_step: Re-iterate through the core GBM model building with optimal...

In RGBM: LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction

Re-iterate through the core GBM model building with optimal set of Tfs for each target gene

Description

This function re-performs the core GBM model building (only one time) using the optimal set of transcription factors obtained from select_ideal_k followed by get_colids for individual target gene to return a regularized GRN.

Usage

second_GBM_step(E, K, df_colids, tfs, targets, Ntfs, Ntargets, lf,  M,  nu, s_f)

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.
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.
df_colids	A matrix made up of column vectors where each column vector represents the optimal set of active Tfs which regulate each target gene and obtained from get_colids. Some column vectors are just made up of zeros indicating that corresponding target genes are isolated and not regulated by any Tf
tfs	List of names of transcription factors.
targets	List of names of target genes.
Ntfs	Total number of transcription factors used in the experiment.
Ntargets	Total number of target genes used in the experiment
lf	Loss Function: 1 -> Least Squares and 2 -> Least Absolute Deviation
M	Number of extensions in boosting model, e.g. number of iterations of the main loop of RGBM algorithm. By default it's 5000.
nu	Shrinkage factor, learning rate, 0<nu<=1. Each extension to boosting model will be multiplied by the learning rate. By default it's 0.001.
s_f	Sampling rate of transcription factors, 0<s_f<=1. Fraction of transcription factors from E, as indicated by tfs vector, which will be sampled without replacement to calculate each extesion in boosting model. By default it's 0.3.

Value

Returns a regularized GRN of the form Ntfs-by-Ntargets

Author(s)

Raghvendra Mall <[e-mail]>

See Also

first_GBM_step

RGBM documentation built on April 14, 2023, 9:10 a.m.