Learn R Programming

RGBM (version 1.0-10)

LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction

Description

Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

Copy Link

Version

Install

install.packages('RGBM')

Monthly Downloads

629

Version

1.0-10

License

GPL (>= 3)

Maintainer

Raghvendra Mall

Last Published

September 26th, 2022

Functions in RGBM (1.0-10)

Regularized Gradient Boosting Machine for inferring GRN

get_ko_experiments

Get indices of experiments where knockout or knockdown happened

Identifies the optimal value of k i.e. top k Tfs for each target gene

second_GBM_step

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

regularized_GBM_step

Perform the regularized GBM modelling once the initial GRN is inferred

regulate_regulon_size

Regulate the size of the regulon for each TF

Get the indices of all the TFs from the data

Generates a matrix S2 of size Ntfs x Ntargets using the null-mutant zscore algorithm Prill, Robert J., et al

normalize_matrix_colwise

Column normalize the obtained adjacency matrix

null_model_refinement_step

Perform the null model refinement step

train_regression_stump_R

Train the regression stump

test_regression_stump_R

Test the regression model

Generate filepaths to maintain adjacency matrices and images

transform_importance_to_weights

Log transforms the edge-weights in the inferred GRN

Convert adjacency matrix to a list of edges

Get the indices of recitifed list of Tfs for individual target gene

Calculate Gene Regulatory Network from Expression data using either LS-TreeBoost or LAD-TreeBoost

Test rgbm predictor

apply_row_deviation

Apply row-wise deviation on the inferred GRN

consider_previous_information

Remember the intermediate inferred GRN while generating the final inferred GRN

Train GBM predictor

Perform either LS-Boost or LAD-Boost (GBM) on expression matrix E followed by the null_model_refinement_step

Add row and column names to the adjacency matrix A

Train RGBM predictor

Test GBM predictor