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HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis
Mingyang Ren1,2, Sanguo Zhang1,2, Qingzhao Zhang3
1School of Mathematics Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Bioinformatics (Oxford, England)
|February 27, 2021
Summary
This study introduces HeteroGGM, an R package for analyzing disease heterogeneity using Gaussian graphical models. It offers an efficient and user-friendly tool for network-based heterogeneity analysis in complex human diseases.
Area of Science:
- Computational biology
- Bioinformatics
- Systems biology
Background:
- Disease heterogeneity is a key challenge in complex human diseases.
- Traditional heterogeneity analysis relies on basic statistics, limiting insight.
- Network-based approaches offer a more comprehensive understanding by considering variable interconnections.
Purpose of the Study:
- To develop a user-friendly and portable software package for Gaussian graphical model (GGM)-based heterogeneity analysis.
- To facilitate broader application of advanced network-based methods for studying disease heterogeneity.
- To provide informative summaries and graphical presentations of heterogeneity analysis results.
Main Methods:
- Development of the R package 'HeteroGGM'.
- Implementation of advanced penalization techniques for GGM construction.
- Integration of methods for summary and graphical presentation of results.
Main Results:
- The HeteroGGM package provides an efficient and portable solution for GGM-based heterogeneity analysis.
- The package enables informative summary statistics and graphical representations.
- Facilitates advanced network-based analysis of complex human diseases.
Conclusions:
- HeteroGGM enhances the accessibility and application of network-based heterogeneity analysis.
- The package supports researchers in exploring complex disease patterns through GGM.
- Promotes advanced computational approaches in disease heterogeneity research.
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