Drug Response Prediction from Differential Multi-Omics Networks
[INTERNAL] Calls a python script to calculate interaction score for co...
[INTERNAL] Check connection
[INTERNAL] Check drug target interaction data
[INTERNAL] Check drug target and layer data
Check pipeline input data for required format
[INTERNAL] Check layer input
[INTERNAL] Check connection and layer data
[INTERNAL] Create chunks from a vector for parallel computing
[INTERNAL] Create chunks from two vectors for parallel computing
[INTERNAL] Combine graphs by adding inter-layer edges
Computes correlation matrices for specified network layers
Calculate drug response score
[INTERNAL] Compute p-values for upper triangle of correlation matrix i...
[INTERNAL] Assign node IDs to the biological identifiers across a grap...
Determine drug target nodes in network
Create global settings variable for DrDimont pipeline
[INTERNAL] Filter drug target nodes
Combines individual layers to a single graph
Compute difference of interaction score of two groups
Builds graphs from specified network layers
Computes interaction score for combined graphs
[INERNAL] Generate a reduced iGraph from adjacency matrices
[INTERNAL] Fetch layer by name from layer object
[INTERNAL] Get layer (and group) settings
[INTERNAL] Analysis of metrics of an iGraph object
Installs python dependencies needed for interaction score computation
[INTERNAL] Inter layer connections by identifiers
[INTERNAL] Interaction table to iGraph graph object
[INTERNAL] Loads output of python script for interaction score calcula...
Specify connection between two individual layers
Reformat drug-target-interaction data
Creates individual molecular layers from raw data and unique identifie...
[INTERNAL] Reduce the the entries in an adjacency matrix by thresholdi...
[INTERNAL] Reduces network based on WGCNA::pickHardThreshold function
Pipe operator
Return detected errors in the input data
Execute all DrDimont pipeline steps sequentially
[INTERNAL] Sample size for correlation computation
[INTERNAL] Create and register cluster
[INTERNAL] Shutdown cluster and remove corresponding connections
[INTERNAL] Get edges adjacent to target nodes
[INTERNAL] Write edge lists and combined graphs to files
While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only to the software and explicitly not to the included data.