ddeweerd/modalyser repository overview

Research in network medicine has to a high extent focused on developing new algorithms for inference of disease modules to come up with “gold standard” approaches in this field. However, it remains challenging to translate these modules into clinical applications by for example utilizing them to uncover biomarkers associated with diseases. A potential reason for this include that modules often are large and nonspecific, often covering plenty of genes which could not be associated directly using the omic that was used for detection. Using modules as a first coarse-graining basis for extracting most relevant disease-associated genes by integrating knowledge about regulatory mechanisms could potentially explain the driving force behind the disease processes or the effect of a certain treatment.

We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease associated genes. The MODalyseR pipeline integrates and extends previously published software packages MODifieR and ComHub and provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data.

MODalyseR is a standalone interactive R Shiny web application implemented in the golem framework and shipped as a Docker image that can be run on a local machine. Persistent storage of input and results is provided by an SQLite database integrated in the application. The docker image includes Cytoscape version 3.8.2 for network visualization. MODalyseR incorporates eight disease module inference methods previously described in the MODifieR R package. In addition, the software includes hub detection from the python package ComHub to infer hub regulators of the disease modules.

Installation instructions and user guide available at:

https://gustafsson-lab.gitlab.io/MODalyseR/⁠