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NeatMap--non-clustering heat map alternatives in R
1Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801-3080, USA. satwik.rajaram@utsouthwestern.edu
BMC Bioinformatics
|January 26, 2010
Summary
NeatMap is a new R package that enhances genomic data visualization by combining dimension reduction with heat map-like displays. It offers a more informative alternative to traditional clustered heat maps, preserving data topology and relationships.
Area of Science:
- Genomics
- Bioinformatics
- Data Visualization
Background:
- Clustered heat maps are popular for genomic data but can obscure intrinsic relationships due to reliance on cluster analysis.
- Dimension reduction algorithms (e.g., PCA, MDS) preserve data topology but lack user-friendly visualizations comparable to heat maps.
- Existing visualization methods often fail to display entire datasets or integrate cluster analysis results effectively.
Purpose of the Study:
- To introduce NeatMap, an R package designed to bridge the gap between dimension reduction techniques and effective data visualization.
- To provide a user-friendly tool that visualizes genomic data using dimension reduction while retaining the intuitive display of heat maps.
- To enable mutual validation by allowing the superimposition of cluster analysis results onto dimension reduction plots.
Main Methods:
- Development of the NeatMap R package, offering novel 2D and 3D plotting capabilities.
- Integration of dimension reduction algorithms (e.g., PCA, MDS) with enhanced visualization techniques.
- Implementation of features to display entire datasets and overlay cluster analysis results.
Main Results:
- NeatMap provides novel visualizations for dimension reduction techniques, displaying the entire dataset and inter-element relationships.
- The package allows for the superimposition of cluster analysis results for validation purposes.
- Comparative analysis using two microarray datasets demonstrated NeatMap's superior informativeness over traditional clustered heat maps.
Conclusions:
- NeatMap retains the strengths of clustered heat maps while mitigating their weaknesses by incorporating dimension reduction.
- The R package facilitates the adoption of non-clustering dimension reduction algorithms for genomic data analysis.
- NeatMap offers a more accurate and insightful representation of genomic data structures and relationships.
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