Drug Response Prediction from Differential Multi-Omics Networks

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Documentation for package ‘DrDimont’ version 0.1.4

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check_input Check pipeline input data for required format
combined_graphs_example Combined graphs
compute_correlation_matrices Computes correlation matrices for specified network layers
compute_drug_response_scores Calculate drug response score
correlation_matrices_example Correlation matrices
determine_drug_targets Determine drug target nodes in network
differential_graph_example Differential graph
drdimont_settings Create global settings variable for DrDimont pipeline
drug_gene_interactions Drug-gene interactions
drug_response_scores_example Drug response score
drug_target_edges_example Drug target nodes in combined network
generate_combined_graphs Combines individual layers to a single graph
generate_differential_score_graph Compute difference of interaction score of two groups
generate_individual_graphs Builds graphs from specified network layers
generate_interaction_score_graphs Computes interaction score for combined graphs
individual_graphs_example Individual graphs
install_python_dependencies Installs python dependencies needed for interaction score computation
interaction_score_graphs_example Interaction score graphs
layers_example Formatted layers object
make_connection Specify connection between two individual layers
make_drug_target Reformat drug-target-interaction data
make_layer Creates individual molecular layers from raw data and unique identifiers
metabolite_data Metabolomics data
metabolite_protein_interactions Metabolite protein interaction data
mrna_data mRNA expression data
phosphosite_data Phosphosite data
protein_data Protein data
return_errors Return detected errors in the input data
run_pipeline Execute all DrDimont pipeline steps sequentially