Package: RGBM 1.0-11

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

Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

Authors:Raghvendra Mall [aut, cre], Khalid Kunji [aut], Melissa O'Neill [ctb]

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manual.pdf |manual.html✨
card.svg |card.png
RGBM/json (API)

# Install 'RGBM' in R:
install.packages('RGBM', repos = c('https://raghvendra5688.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 4 scripts 707 downloads 1 mentions 25 exports 6 dependencies

Last updated from:80c5df56a5. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK110
linux-devel-x86_64OK110
source / vignettesOK156
linux-release-arm64OK102
linux-release-x86_64OK108
macos-release-arm64OK104
macos-release-x86_64OK231
macos-oldrel-arm64OK93
macos-oldrel-x86_64OK248
windows-develOK97
windows-releaseOK94
windows-oldrelOK95
wasm-releaseOK88

Exports:add_namesapply_row_deviationconsider_previous_informationfirst_GBM_stepGBMGBM.testGBM.trainget_colidsget_filepathsget_ko_experimentsget_tf_indicesnormalize_matrix_colwisenull_model_refinement_stepregularized_GBM_stepregulate_regulon_sizeRGBMRGBM.testRGBM.trainsecond_GBM_stepselect_ideal_ktest_regression_stump_Rtrain_regression_stump_Rtransform_importance_to_weightsv2lz_score_effect

Dependencies:codetoolsdoParallelforeachiteratorsplyrRcpp

Readme and manuals

Help Manual

Help pageTopics
Add row and column names to the adjacency matrix Aadd_names
Apply row-wise deviation on the inferred GRNapply_row_deviation
Remember the intermediate inferred GRN while generating the final inferred GRNconsider_previous_information
Perform either LS-Boost or LAD-Boost ('GBM') on expression matrix E followed by the 'null_model_refinement_step'first_GBM_step
Calculate Gene Regulatory Network from Expression data using either LS-TreeBoost or LAD-TreeBoostGBM
Test GBM predictorGBM.test
Train GBM predictorGBM.train
Get the indices of recitifed list of Tfs for individual target geneget_colids
Generate filepaths to maintain adjacency matrices and imagesget_filepaths
Get indices of experiments where knockout or knockdown happenedget_ko_experiments
Get the indices of all the TFs from the dataget_tf_indices
Column normalize the obtained adjacency matrixnormalize_matrix_colwise
Perform the null model refinement stepnull_model_refinement_step
Perform the regularized GBM modelling once the initial GRN is inferredregularized_GBM_step
Regulate the size of the regulon for each TFregulate_regulon_size
Regularized Gradient Boosting Machine for inferring GRNRGBM
Test rgbm predictorRGBM.test
Train RGBM predictorRGBM.train
Re-iterate through the core GBM model building with optimal set of Tfs for each target genesecond_GBM_step
Identifies the optimal value of k i.e. top k Tfs for each target geneselect_ideal_k
Test the regression modeltest_regression_stump_R
Train the regression stumptrain_regression_stump_R
Log transforms the edge-weights in the inferred GRNtransform_importance_to_weights
Convert adjacency matrix to a list of edgesv2l
Generates a matrix S2 of size Ntfs x Ntargets using the null-mutant zscore algorithm 'Prill, Robert J., et al'z_score_effect