Clinical chemistry data are routinely generated as part of preclinical animal toxicity studies and human clinical studies. With large-scale studies involving hundreds or even thousands of samples in multiple treatment groups, it is currently difficult to interpret the resulting complex, high-density clinical chemistry data. Accordingly, we conducted this study to investigate methods for easy visualization of complex, high-density data. Clinical chemistry data were obtained from male rats each treated with one of eight different acute hepatotoxicants from a large-scale toxicogenomics study. The raw data underwent a Z-score transformation comparing each individual animal's clinical chemistry values to that of reference controls from all eight studies and then were visualized in a single graphic using a heat map. The utility of using a heat map to visualize high-density clinical chemistry data was explored by clustering changes in clinical chemistry values for >400 animals. A clear distinction was observed in animals displaying hepatotoxicity from those that did not. Additionally, while animals experiencing hepatotoxicity showed many similarities in the observed clinical chemistry alterations, distinct differences were noted in the heat map profile for the different compounds. Using a heat map to visualize complex, high-density clinical chemistry data in a single graphic facilitates the identification of previously unrecognized trends. This method is simple to implement and maintains the biological integrity of the data. The value of this clinical chemistry data transformation and visualization will manifest itself through integration with other high-density data, such as genomics data, to study physiology at the systems level.
A Comparative CFD Study of Two Air Distribution Systems with Hot Aisle Containment in High-Density Data Centers