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 |