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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
RGBM.test

Test rgbm predictor
RGBM.train

Train RGBM predictor
get_ko_experiments

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

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