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RGBM (version 1.0-7)

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). See Mall et al (2017) .

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Version

Install

install.packages('RGBM')

Monthly Downloads

629

Version

1.0-7

License

GPL (>= 3)

Maintainer

Raghvendra Mall

Last Published

May 8th, 2017

Functions in RGBM (1.0-7)

GBM.train

Train GBM predictor

RGBM

Regularized Gradient Boosting Machine for inferring GRN

Get indices of experiments where knockout or knockdown happened

get_tf_indices

Get the indices of all the TFs from the data

consider_previous_information

Remember the intermediate inferred GRN while generating the final inferred GRN

first_GBM_step

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

get_colids

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

get_filepaths

Generate filepaths to maintain adjacency matrices and images

test_regression_stump_R

Test the regression model

train_regression_stump_R

Train the regression stump

normalize_matrix_colwise

Column normalize the obtained adjacency matrix

null_model_refinement_step

Perform the null model refinement step

transform_importance_to_weights

Log transforms the edge-weights in the inferred GRN

add_names

Add row and column names to the adjacency matrix A

apply_row_deviation

Apply row-wise deviation on the inferred GRN

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

GBM

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

GBM.test

Test GBM predictor

second_GBM_step

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

select_ideal_k

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

z_score_effect

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

v2l

Convert adjacency matrix to a list of edges