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

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Documentation for package ‘RGBM’ version 1.0-11

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add_names Add row and column names to the adjacency matrix A
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
first_GBM_step Perform either LS-Boost or LAD-Boost ('GBM') on expression matrix E followed by the 'null_model_refinement_step'
GBM Calculate Gene Regulatory Network from Expression data using either LS-TreeBoost or LAD-TreeBoost
GBM.test Test GBM predictor
GBM.train Train GBM predictor
get_colids Get the indices of recitifed list of Tfs for individual target gene
get_filepaths Generate filepaths to maintain adjacency matrices and images
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
normalize_matrix_colwise Column normalize the obtained adjacency matrix
null_model_refinement_step Perform the null model refinement step
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
RGBM Regularized Gradient Boosting Machine for inferring GRN
RGBM.test Test rgbm predictor
RGBM.train Train RGBM 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
test_regression_stump_R Test the regression model
train_regression_stump_R Train the regression stump
transform_importance_to_weights Log transforms the edge-weights in the inferred GRN
v2l Convert adjacency matrix to a list of edges
z_score_effect Generates a matrix S2 of size Ntfs x Ntargets using the null-mutant zscore algorithm Prill, Robert J., et al