Man pages for RGBM
LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction
| 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... |
| first_GBM_step | Perform either LS-Boost or LAD-Boost ('GBM') on expression... |
| GBM | Calculate Gene Regulatory Network from Expression data using... |
| GBM.test | Test GBM predictor |
| GBM.train | Train GBM predictor |
| get_colids | Get the indices of recitifed list of Tfs for individual... |
| get_filepaths | Generate filepaths to maintain adjacency matrices and images |
| get_ko_experiments | Get indices of experiments where knockout or knockdown... |
| 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... |
| 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... |
| select_ideal_k | Identifies the optimal value of k i.e. top k Tfs for each... |
| 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... |
