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)
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.
install.packages("DrDimont") to install the package and the R dependencies (v0.1.3 available)devtools::install() within R to install the package, or use remotes::install_gitlab("PHiort/DrDimont") without cloning.DrDimont::install_python_dependencies() to install the necessary dependencies automatically. You can use the function arguments to customize and use either pip or conda for the installation. If you prefer to install the dependencies manually, check out the requirements files in this repository inst/requirements_pip.txt, and inst/requirements_conda.txt.An exemplary pipeline execution with the included data can be found in doc/DrDimont_Vignette.html and at https://cran.r-project.org/web/packages/DrDimont/vignettes/DrDimont_Vignette.html.
The supplied case study data uses data published by Krug et al. (2020) (https://www.doi.org/10.1016/j.cell.2020.10.036). The package license applies only to the software and explicitly not to the included data.
At the moment all functions are exported to make debugging easier. However, many functions are not intended for user-interaction. These functions are marked with [INTERNAL] in the function documentation.
The package DrDimont is an updated version of the previously published molnet package (https://github.com/molnet-org/molnet)