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A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional
1Department of Food Science and Technology, University of California, Davis, One Shields Ave, Davis, CA 95616, USA.
Life (Basel, Switzerland)
|November 26, 2022
Summary
Researchers can now perform complex omics data analysis and visualization rapidly using the new MATLAB-based "Ana" program. This tool simplifies statistical analysis for food chemistry and nutrition, reducing manual processing time.
Area of Science:
- Food chemistry and nutrition science
- Bioinformatics and computational biology
- Data science and statistical analysis
Background:
- Omics studies (phytochemical, lipidomics, proteomics, metabolomics, glycomics) are vital in food science.
- Traditional data processing is labor-intensive and requires coding expertise, posing a barrier for many researchers.
- A need exists for user-friendly tools to streamline omics data analysis.
Purpose of the Study:
- To develop an integrated MATLAB-based program for rapid omics data visualization and statistical analysis.
- To provide a user-friendly solution for researchers without extensive coding backgrounds.
- To generate publication-quality figures for omics datasets.
Main Methods:
- Developed a MATLAB-based program named 'Ana' integrating data visualization and statistical analysis.
- Implemented a three-in-one method for processing omics data from Excel files.
- Utilized techniques including 3D heatmap, hierarchical clustering, and principal component analysis (PCA).
Main Results:
- The 'Ana' program processed a 7 samples * 22 compounds omics dataset in under 20 seconds on standard PCs.
- Generated high-quality figures (up to 300 dpi) suitable for publication and presentation.
- Successfully differentiated omics datasets using color codes and bar size adjustments in visualizations.
Conclusions:
- The 'Ana' program offers a rapid, efficient, and accessible method for omics data analysis and visualization.
- It significantly reduces the time and effort required for manual or code-based data processing.
- Provides valuable training resources for students to learn statistical data analysis in omics research.
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