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Published on: January 16, 2019
Heat map visualization of high-density clinical chemistry data
J Todd Auman1, Gary A Boorman, Ralph E Wilson
1National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Physiological Genomics
|July 27, 2007
Summary
This study introduces a heatmap visualization method for complex clinical chemistry data from toxicity studies. This approach simplifies data interpretation, clearly distinguishing between toxic and non-toxic animal groups and revealing compound-specific patterns.
Area of Science:
- Toxicology
- Data Visualization
- Biochemistry
Background:
- Clinical chemistry data are crucial in preclinical and clinical studies but are challenging to interpret at high densities.
- Large-scale studies generate complex datasets that hinder easy analysis and trend identification.
Purpose of the Study:
- To develop and evaluate a method for the easy visualization of complex, high-density clinical chemistry data.
- To assess the utility of heat maps for interpreting data from large-scale toxicity studies.
Main Methods:
- Clinical chemistry data from male rats exposed to eight different hepatotoxicants were analyzed.
- Data were Z-score transformed, comparing individual animal values to control groups.
- Transformed data were visualized using heat maps, with clustering applied to >400 animals.
Main Results:
- Heat maps clearly distinguished animals exhibiting hepatotoxicity from those that did not.
- While hepatotoxic animals showed similar alterations, distinct heat map profiles emerged for different compounds.
- The visualization method facilitated the identification of previously unrecognized trends in clinical chemistry data.
Conclusions:
- Heat map visualization offers a simple yet effective method for interpreting complex, high-density clinical chemistry data.
- This approach maintains data integrity and aids in identifying toxicity patterns and compound-specific effects.
- Integration with other high-density data, like genomics, can advance systems-level physiological studies.
