Man pages for RGBM
LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction

add_namesAdd row and column names to the adjacency matrix A
apply_row_deviationApply row-wise deviation on the inferred GRN
consider_previous_informationRemember the intermediate inferred GRN while generating the...
first_GBM_stepPerform either LS-Boost or LAD-Boost ('GBM') on expression...
GBMCalculate Gene Regulatory Network from Expression data using...
GBM.testTest GBM predictor
GBM.trainTrain GBM predictor
get_colidsGet the indices of recitifed list of Tfs for individual...
get_filepathsGenerate filepaths to maintain adjacency matrices and images
get_ko_experimentsGet indices of experiments where knockout or knockdown...
get_tf_indicesGet the indices of all the TFs from the data
normalize_matrix_colwiseColumn normalize the obtained adjacency matrix
null_model_refinement_stepPerform the null model refinement step
regularized_GBM_stepPerform the regularized GBM modelling once the initial GRN is...
regulate_regulon_sizeRegulate the size of the regulon for each TF
RGBMRegularized Gradient Boosting Machine for inferring GRN
RGBM.testTest rgbm predictor
RGBM.trainTrain RGBM predictor
second_GBM_stepRe-iterate through the core GBM model building with optimal...
select_ideal_kIdentifies the optimal value of k i.e. top k Tfs for each...
test_regression_stump_RTest the regression model
train_regression_stump_RTrain the regression stump
transform_importance_to_weightsLog transforms the edge-weights in the inferred GRN
v2lConvert adjacency matrix to a list of edges
z_score_effectGenerates a matrix S2 of size Ntfs x Ntargets using the...
RGBM documentation built on Jan. 9, 2018, 5:03 p.m.