DiNetxify

DiNetxify is an open-source Python package for comprehensive three-dimensional (3D) disease network analysis of large-scale electronic health record (EHR) data. It integrates data harmonization, analysis, and visualization into a user-friendly package to uncover multimorbidity patterns and disease progression pathways. DiNetxify is optimized for efficiency (capable of handling cohorts of hundreds of thousands of patients within hours on standard hardware) and supports multiple study designs with customizable parameters and parallel computing. DiNetxify is released under GPL-3.0 license.

Installation

DiNetxify requires Python 3.10+. Install the latest release from PyPI using pip:

pip install dinetxify

Source code and issue report

Available on Github, HZcohort/DiNetxify. Please report bugs and issues there.

Citation

If you use this software in your research, please cite the following papers:

1. Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel three-dimensional disease network approach (PMID: 40681841)

Contact

• Can Hou: houcan@wchscu.cn

• Haowen Liu: haowenliu81@gmail.com

Documentation

• Input data preparation
  • Requirements for input data
  • Dummy dataset overview
• Data harmonization
  • Initializing the data object
  • Load phenotype data
  • Load medical record data
  • Save DiseaseNetworkData object
  • Reload DiseaseNetworkData object
• Three-dimensional disease network analysis
  • One-step analysis
  • Step-by-step analysis
• Visualization
  • Initializing the Plot object
  • PheWAS plot
  • Comorbidity network plot
  • Disease trajectory plot
  • Three-dimensional plot
• Result tables description
  • Result of PheWAS analysis
  • Result of comorbidity strength estimation
  • Result of binomial test
  • Result of comorbidity network analysis
  • Result of disease trajectory analysis
• API Reference
  • Class DiseaseNetworkData
  • Analysis Functions
  • Class Plot
• Changelog