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pairheatmap: comparing expression profiles of gene groups in heatmaps.
1Eugene Mcdermott Center For Human Growth and Development, The University of Texas Southwestern Medical Center, 6000 Harry Hines Boulevard, Dallas, TX 75390, USA.
Computer Methods and Programs in Biomedicine
|September 11, 2013
Summary
This study introduces pairheatmap, a new R software tool for comparing gene expression patterns between two heatmaps. It visualizes changes using conditioning variables and separate clustering, aiding bioinformatics analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Visualization
Background:
- Gene expression analysis often requires comparing patterns across different conditions or time points.
- Existing visualization tools may lack the flexibility to directly compare two heatmaps with integrated pattern change analysis.
Purpose of the Study:
- To introduce pairheatmap, a novel R package for visualizing and comparing gene expression patterns between two heatmaps.
- To provide a flexible framework for analyzing pattern changes influenced by conditioning variables like time.
- To facilitate the integration of heatmap comparison into bioinformatics pipelines.
Main Methods:
- Development of the pairheatmap software in the R statistical environment.
- Implementation of a flexible framework for generating and comparing two heatmaps.
- Inclusion of a conditioning variable (e.g., time) to visualize dynamic pattern changes.
- Application of separate clustering for row groups to highlight differential patterns.
- Generation of high-quality figures using the R package 'grid'.
Main Results:
- pairheatmap enables the direct comparison of two heatmaps, facilitating the identification of differential gene expression patterns.
- The software allows for the visualization of pattern changes over time or other conditioning variables.
- Separate clustering options enhance the ability to discern group-specific pattern alterations.
- The generated figures are of high quality, suitable for publication and presentation.
- The architecture is designed for efficient incorporation into existing bioinformatics workflows.
Conclusions:
- pairheatmap offers a powerful and flexible solution for comparative gene expression analysis using heatmaps.
- The tool enhances the visualization of dynamic gene expression patterns and facilitates the discovery of biologically relevant changes.
- Its integration capabilities make it a valuable asset for modern bioinformatics research.
