The moDiNA pipeline facilitates multi-omics Differential Network Analysis in mixed-type data. It compares two biologically distinct contexts and constructs a ranked differential network that captures both differentially abundant variables (nodes) and differential associations (edges).
The framework supports multiple data types, including continuous, binary, nominal, and ordinal categorical variables. All processing steps are configurable through a user-defined configuration file, allowing flexible adaptation to different datasets and analysis goals.
Full documentation is available at https://dyhealthnet.github.io/moDiNA.
moDiNA can be installed using Conda, Docker, or from source.
If you face any issues, feel free to open a GitHub issue.
Requirements: Python 3.11
Currently, the moDiNA package is only available on GitHub.
It is recommended to install moDiNA in a clean Conda (Miniconda) environment.
We suggest using Mamba, a faster drop-in replacement for Conda that improves dependency resolution.
Mamba is automatically installed when using Miniforge.
First, follow the installation instructions for your operating system.
Then create and activate a new environment:
mamba create -n modina_env python=3.11
mamba activate modina_envNext, install the package:
pip install git+https://github.com/DyHealthNet/moDiNA.gitmoDiNA is available as Docker image.
Pull the image:
docker pull ghrc.io/dyhealthnet/modina:latestRun the image:
docker run -it ghrc.io/dyhealthnet/modina:latestTo install moDiNA from source, clone the repository and install the package using pip:
git clone https://github.com/DyHealthNet/moDiNA.git
cd moDiNA
pip install -e .Based on an extensive benchmark analysis performed on simulated data, we recommend the following pipeline configuration:
- Filtering: For reasonably small datasets, no filtering is required. For high-dimensional data, filtering can substantially reduce the runtime of moDiNA.
- Edge Metric: pre-LS
- Node Metric: STC
- Ranking Algorithm: PageRank+