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

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

## Documentation for package ‘RGBM’ version 1.0-11

- DESCRIPTION file

## Help Pages

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'