LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction
Add row and column names to the adjacency matrix A
Apply row-wise deviation on the inferred GRN
Remember the intermediate inferred GRN while generating the final infe...
Perform either LS-Boost or LAD-Boost (GBM) on expression matrix E fo...
Calculate Gene Regulatory Network from Expression data using either LS...
Test GBM predictor
Train GBM predictor
Get the indices of recitifed list of Tfs for individual target gene
Generate filepaths to maintain adjacency matrices and images
Get indices of experiments where knockout or knockdown happened
Get the indices of all the TFs from the data
Column normalize the obtained adjacency matrix
Perform the null model refinement step
Perform the regularized GBM modelling once the initial GRN is inferred
Regulate the size of the regulon for each TF
Regularized Gradient Boosting Machine for inferring GRN
Test rgbm predictor
Train RGBM predictor
Re-iterate through the core GBM model building with optimal set of Tfs...
Identifies the optimal value of k i.e. top k Tfs for each target gene
Test the regression model
Train the regression stump
Log transforms the edge-weights in the inferred GRN
Convert adjacency matrix to a list of edges
Generates a matrix S2 of size Ntfs x Ntargets using the null-mutant zs...
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).