Drug Response Prediction from Differential Multi-Omics Networks

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Documentation for package ‘DrDimont’ version 0.1.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

calculate_interaction_score	[INTERNAL] Calls a python script to calculate interaction score for combined graphs
check_connection	[INTERNAL] Check connection
check_drug_target	[INTERNAL] Check drug target interaction data
check_drug_targets_in_layers	[INTERNAL] Check drug target and layer data
check_input	Check pipeline input data for required format
check_layer	[INTERNAL] Check layer input
check_sensible_connections	[INTERNAL] Check connection and layer data
chunk	[INTERNAL] Create chunks from a vector for parallel computing
chunk_2gether	[INTERNAL] Create chunks from two vectors for parallel computing
combined_graphs_example	Combined graphs
combine_graphs	[INTERNAL] Combine graphs by adding inter-layer edges
compute_correlation_matrices	Computes correlation matrices for specified network layers
compute_drug_response_scores	Calculate drug response score
corPvalueStudentParallel	[INTERNAL] Compute p-values for upper triangle of correlation matrix in parallel
correlation_matrices_example	Correlation matrices
create_unique_layer_node_ids	[INTERNAL] Assign node IDs to the biological identifiers across a graph layer
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
find_targets	[INTERNAL] Filter drug target nodes
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
generate_reduced_graph	[INERNAL] Generate a reduced iGraph from adjacency matrices
get_layer	[INTERNAL] Fetch layer by name from layer object
get_layer_setting	[INTERNAL] Get layer (and group) settings
graph_metrics	Analysis of metrics of an iGraph object
individual_graphs_example	Individual graphs
install_python_dependencies	Installs python dependencies needed for interaction score computation
interaction_score_graphs_example	Interaction score graphs
inter_layer_edgelist_by_id	[INTERNAL] Inter layer connections by identifiers
inter_layer_edgelist_by_table	[INTERNAL] Interaction table to iGraph graph object
layers_example	Formatted layers object
load_interaction_score_output	[INTERNAL] Loads output of python script for interaction score calculation
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
network_reduction_by_pickHardThreshold	[INTERNAL] Reduces network based on WGCNA::pickHardThreshold function
network_reduction_by_p_value	[INTERNAL] Reduce the the entries in an adjacency matrix by thresholding on p-values
phosphosite_data	Phosphosite data
protein_data	Protein data
return_errors	Return detected errors
run_pipeline	Execute all DrDimont pipeline steps sequentially
sample_size	[INTERNAL] Sample size for correlation computation
set_cluster	[INTERNAL] Create and register cluster
shutdown_cluster	[INTERNAL] Shutdown cluster and remove corresponding connections
target_edge_list	[INTERNAL] Get edges adjacent to target nodes
write_interaction_score_input	[INTERNAL] Write edge lists and combined graphs to files