kboost: A function to run KBoost.

In Luisiglm/KBoost: Inference of gene regulatory networks from gene expression data

Description

A function to run KBoost.

Usage

kboost(X, TFs, g, v, prior_weights, ite)

Arguments

X	an NxG matrix with the expression values of G genes and N obvs..
TFs	a Kx1 numeric matrix with integers of columns of X that are TFs.
g	a positive no., width parameter for RBF kernel. (default g = 40)
v	a no. between 0 and 1 with the shrinkage parameter. (default v =0.1)
prior_weights	it can be a scalar or GxK. (default 0.5)
ite	an integer for the maximum number of iterations (default 3)

Value

a list with the results for kboost, with fields: GRN a matrix with the posterior edge probability after network refinement. GRN_UP a matrix with the posterior edges before refinement. model a matrix with logical values for the TFs selected for each gene. g the width parameter for the RBF kernel. v the shrinkage parameter. prior the prior of each model. TFs a matrix with integers of each gene that is a TF. prior_weights the prior_weights with which KBoost was run. run_time a sacalar with the running time.

Examples

data(D4_multi_1)
Net <- kboost(D4_multi_1)

Luisiglm/KBoost documentation built on May 13, 2021, 7:27 p.m.