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BioNetStat: A Tool for Biological Networks Differential Analysis
Vinícius Carvalho Jardim1,2, Suzana de Siqueira Santos1, Andre Fujita1
1Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
Frontiers in Genetics
|July 12, 2019
Summary
BioNetStat is a new tool for comparing multiple biological networks, unlike methods that only compare two. It identifies significant network changes and key biological components across various states.
Area of Science:
- Systems biology
- Network science
- Bioinformatics
Background:
- Biological interactions are often studied using network theory, typically comparing only two states (e.g., healthy vs. diseased).
- Biological systems frequently exhibit more than two states (e.g., different tumor grades), necessitating methods for multi-state network comparison.
- Existing methods may not adequately capture complex biological system dynamics across numerous states.
Purpose of the Study:
- To introduce BioNetStat, a Bioconductor package with a graphical interface for simultaneous comparison of multiple biological networks.
- To enable the analysis of structural alterations in biological networks across diverse states using network features like centrality measures.
- To provide a user-friendly tool for identifying significant changes in biological systems represented by networks.
Main Methods:
- Development of BioNetStat, a Bioconductor package utilizing network theory and graphical analysis.
- Comparison of correlation networks based on the probability distribution of graph features, such as centrality measures.
- Evaluation of BioNetStat performance using simulated data and two case studies (tumor gene expression and plant metabolism).
Main Results:
- BioNetStat demonstrates statistical power that is less sensitive to an increasing number of networks compared to Gene Set Coexpression Analysis (GSCA).
- The tool successfully identifies nodes with altered centrality, indicating changes in biological component relevance across states.
- BioNetStat detected altered networks associated with signaling pathways missed by other comparative methods.
Conclusions:
- BioNetStat offers a robust approach for comparing multiple biological networks, advancing systems biology research.
- The package provides valuable insights into structural network modifications and identifies key biological players across various states.
- BioNetStat enhances the ability to discover biologically relevant pathways and molecular interactions in complex systems.
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