TDAview: an online visualization tool for topological data analysis.

Area of Science:

• Computational Biology
• Data Visualization
• Bioinformatics

Background:

• Topological Data Analysis (TDA) offers powerful methods for understanding complex, high-dimensional datasets.
• The Mapper algorithm is a key TDA technique for revealing underlying data structures.
• Existing tools may require programming expertise, limiting accessibility for biologists and clinicians.

Purpose of the Study:

• To introduce TDAview, a novel online tool for TDA and visualization.
• To provide an accessible platform for biologists and clinicians to apply TDA without coding.
• To facilitate the exploration and visualization of complex biological data.

Main Methods:

• TDAview implements the Mapper algorithm for TDA.
• It offers both analysis and visualization modes, as well as a visualization-only mode.
• The tool supports extensive graph visualization, including metadata display and figure generation.

Main Results:

• TDAview successfully applies the Mapper algorithm to construct and visualize topological graphs.
• The tool allows for interactive data exploration through graphical display of node and edge properties.
• It can handle large datasets, enabling applications like constructing topology-based gene co-expression networks.

Conclusions:

• TDAview democratizes TDA by providing a user-friendly interface for complex data analysis.
• The tool enhances biological discovery by enabling visualization of high-dimensional data structures.
• TDAview is a valuable resource for researchers seeking to leverage TDA in their work.