Heat map visualization of high-density clinical chemistry data

J. Todd Auman, Gary A. Boorman, Ralph E. Wilson, Gregory S. Travlos, Richard S. Paules


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.

  • clinical pathology
  • hierarchical clustering
  • rat
  • hepatotoxicity
  • toxicogenomics

the postgenomic era in science has led to the generation of a number of large, high-density data sets with hundreds to thousands of data points for each tested subject. In the field of toxicology, genomics technologies have been used to investigate how different stresses alter gene expression (see Ref. 12 for a recent review). For many in vivo studies, gene expression changes are measured in the tissue of interest, but other data is also obtained to give a “phenotypic anchor” (7, 11) for the gene changes, including clinical chemistry and histopathology (2). However, for large data sets, including large human clinical/epidemiological studies, it can be problematic to effectively evaluate the phenotypic anchor, due to the sheer number of data points to consider.

Clinical chemistry data are often viewed in a data table or a bar graph, where one can examine the changes that occur for one analyte across the groups of interest. For a study involving only one or a few compounds, these types of visualizations help investigators determine how subjects in each group react to the given stressor. However, for large animal data sets involving multiple compounds, dose groups, and time points it is very difficult to give a meaningful visual representation of the data with traditional bar graphs due to the number of data points that exist in these types of experiments. This is especially true for large human clinical/epidemiological studies, such as the Framingham Heart Study, which has thousands of people enrolled (5). There is obviously a need to visualize high-density clinical chemistry data in a manner that will assist in putting gene expression data, or other high-density data, in the proper biological context. The goal of this study was to develop a method to visualize multiple analytes of clinical chemistry data over many disparate samples (i.e., different compounds, doses and time points) in a single graphic.

To illustrate the usefulness of having a single graphic to visualize complex data, we obtained clinical chemistry data from a compendium of eight hepatotoxicants generated at the National Institute of Environmental Health Sciences. In this compendium, male Fischer rats, ∼12–14 wk of age, were treated with a single dose from one of eight hepatotoxicants or their respective vehicle (Table 1). Serum (250 μl) was obtained 6, 24, and 48 h after dosing for clinical chemistry analysis. Each treatment group contained at least four animals. All animals were treated humanely in accordance to guidelines established in the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals (6). Clinical chemistry analyses were performed using the Roche Cobas Farachemistry analyzer (Roche Diagnostic Systems, Montclair, NJ).

View this table:
Table 1.

Hepatotoxicants used in this study

The main goal for assessing changes in clinical chemistry across many animals in one graphic is to be able to quickly identify which animals show signs of organ damage, as evidenced by clinical chemistry alterations. Therefore, each animal's clinical chemistry measurements were analyzed in relation to values observed in the normal population. Since our study set is large, it was possible to obtain the median and standard deviation of the analytes for all of the vehicle-treated animals at all time points (n = 103) to define the reference value for each analyte (Table 2) within this study.

View this table:
Table 2.

Clinical chemistry values for vehicle-treated animals

After defining the reference value for each analyte, we transformed the data so that the visualization would be an accurate representation of the data. The raw clinical chemistry data needed to be transformed for several reasons. First, several of the analytes have a range of values of over three orders of magnitude, thus making log transformation necessary. Second, the various analytes have vastly different dynamic ranges. For instance, significant liver injury is indicated by a several hundredfold to several thousandfold change in normal serum ALT (alanine aminotransferase) levels, while a greater than twofold change in serum creatinine levels indicates a significant loss of kidney function (4). Therefore, to put the different analytes on the same scale we performed a Z-score transformation on the log transformed values using the median and standard deviation of the vehicle-treated animals for the basis of the transformation. The Z-score transformation [Z = (observed value − baseline median)/baseline standard deviation] ensures that each analyte over the population of animals has a median value of 0 with a standard deviation of 1. We used the median instead of the mean, since the median is less sensitive to statistical outliers. In addition to putting all of the analytes on the same scale, the Z-score transformation also centers the log transformed data on 0, with values greater than the baseline having a positive Z-score and values less than the baseline having a negative Z-score.

The clinical chemistry Z-scores were then used to perform hierarchical clustering using Eisen's Cluster program (1). Eisen's TreeView program (1) was used to visualize the data in a heat map, with yellow indicating Z-scores >0, blue indicating Z-scores <0, black indicating Z-scores ≈0 and gray indicating data not present. Figure 1 illustrates in a heat map the dose-response clinical chemistry alterations elicited by N-nitrosomorpholine. N-nitrosomorpholine induced minimal hepatocyte necrosis in animals dosed with 50 mg/kg, whereas animals dosed with 300 mg/kg N-nitrosomorpholine progress from minimal hepatocyte necrosis 6 h after dosing to marked hepatocyte necrosis 48 h after dosing. The heat map in Fig. 1 readily shows the time-mediated progression of hepatocyte necrosis induced by 300 mg/kg N-nitrosomorpholine, with no apparent elevations of liver enzymes at 6 h (minimal necrosis), moderate increase of liver enzymes at 24 h (minimal to moderate necrosis), and robust elevations at 48 h (marked necrosis). It should also be noted that clinical chemistry alterations do not generally become noticeable until after 6 h, a known limitation of clinical chemistry measurements.

Fig. 1.

Heat map of clinical chemistry data from animals dosed with N-nitrosomorpholine. Heat map visualization of clinical chemistry alterations of rats dosed with N-nitrosomorpholine (or vehicle) following Z-score transformation of the raw data. Yellow colors indicate Z-scores >0, blue colors indicate Z-scores <0, gray indicates missing data, and black indicates Z-scores = 0. The saturation threshold is set at + and −12.00 (see color bar). To the left of the heat map is a gradient bar indicating the degree of hepatocyte necrosis, ranging from black (no necrosis) to bright red (marked hepatocyte necrosis).

To further illustrate the usefulness of a clinical chemistry heat map, we have examined the clinical chemistry alterations in a compendium of eight liver hepatotoxicants. Figure 2 shows the cluster and heat map of all treated animals in this eight-compound study (See Supplemental Fig. S1 for the fully annotated cluster1 ). From the dendrogram for the analytes, it is evident that the liver enzymes cluster tightly together (ALT, AST, LDH, SDH; see Table 2 for full names), providing support that the data transformation is valid since these analytes are markers of hepatocellular damage and increase with liver injury (4). The middle portion of the heat map consists of animals displaying evidence of hepatotoxicity based on the elevation of liver enzymes (ALT, AST, SDH, LDH). Examination of the histopathologic data indicates that these animals displayed moderate to marked hepatocyte necrosis. In general, these animals were exposed to either the high or moderate dose of the hepatotoxic compounds, with the notable exception of the animals dosed with the nonhepatotoxic compound 1,4-dichlorobenzene (9), which did not elicit clinical chemistry changes associated with hepatotoxicity. Examination of several subclusters reveals that hepatotoxic doses of the administered compounds elicit similar alterations in their clinical chemistry panel profiles; however, each compound elicits a pattern of change that is distinct. Thioacetamide shows indications of eliciting nephrotoxicity, in addition to hepatotoxicity, as evidenced by the elevation of blood urea nitrogen and creatinine 48 h after a 150 mg/kg dose (subcluster A, Fig. 2). Subcluster B (Fig. 2) shows a group of animals dosed with five different hepatotoxicants exhibiting similar elevations of ALT, AST, LDH, SDH, and TBA (total bile acids), but dissimilar reductions in serum triglycerides. Diquat appears to elicit a different hepatotoxic response from the other hepatotoxicants, in that no elevations in SDH or TBA are apparent (subcluster C, Fig. 2). On the basis of this heat map, further investigation might be warranted to determine if the differences seen in clinical chemistry alterations between the compounds are also evident at the histopathologic or molecular scale.

Fig. 2.

Clustering and heat map of clinical chemistry alterations. Heat map visualization of clinical chemistry alterations of all treated rats following Z-score transformation of the raw data. Hierarchical clustering was performed on both animals and clinical chemistry analytes. Yellow colors indicate Z-scores >0, blue colors indicate Z-scores <0, gray indicates missing data, and black indicates Z-scores = 0. The saturation threshold is set at + and −8.00 (see color bar). Selected subclusters described in the text have been selected for magnification (subclusters A, B, and C). The fully annotated cluster can be viewed in Supplemental Fig. S1.

Described here, for the first time, is a method that can be used to visualize high-density clinical chemistry data in a single graphic by using a heat map. The need for this type of visualization arises from the prevalence of large experimental data sets that contain hundreds, if not thousands, of data points. Our method makes use of the Z-score transformation to put each animal's clinical chemistry data in the framework of what is normal for an untreated rat, which means a bank of historical data or a large group of concurrent control animals is needed for this type of transformation to work. For animal studies, the reference control needs to be of the same sex, strain, and age as the test animals, with all animals on the same diet, since these factors significantly influence individual animals' clinical chemistry values (4, 8). This type of data transformation should also prove useful for large human clinical data sets, as there are considerable historical data on the reference values for the different clinical chemistry analytes in human populations (10).

The most important facet of this normalization procedure is that the biological context of the clinical chemistry data is maintained. This can be seen in Fig. 2 by the high degree of similarity seen between individual rats in the different treatment groups. Also the tight clustering of ALT, AST, LDH, SDH, and TBA indicates that the biological context of the data is preserved following the data transformation, since these analytes are released into the blood following liver injury (4).

An important advantage of using a heat map to visualize clinical chemistry data across multiple animals and compounds is that patterns in the data can be identified that were not readily discernible when looking at each clinical chemistry parameter or treatment group individually. For instance, it is apparent the serum triglyceride levels often decrease with hepatic damage; however, as can be seen in the heat map, some compounds, interestingly, do not elicit the concomitant decrease in serum triglycerides. Additionally, examination of the heat map indicates that exposure to the highest dose of thioacetamide and N-nitrosomorpholine elicited kidney damage at 48 h after dosing, as seen by the elevation in blood urea nitrogen levels and was confirmed by histopathology (3). However, it is possible that the two compounds elicit different types of kidney damage, since thioacetamide administration led to an increase in serum creatinine, while N-nitrosomorpholine administration did not. Further investigation is warranted to determine how the kidney damage differs between the two compounds.

The greatest value of this clinical chemistry data transformation and visualization likely resides in its integration with other high-density data, such as genomics, proteomics, and metabolomics data. By integrating disparate types of data effectively, while ensuring that the biological meaning in the data is maintained, greater knowledge and insight should be achieved than what can be attained from each type of data by itself. We and others (3) have started the process of integrating disparate data types, which will hopefully provide a clear benefit for the interpretation of high-density data sets.


This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences, and also funded in part with federal funds from the National Institute of Environmental Health Sciences, NIH, under Contract N01-ES-35513.


We thank Drs. Robert Maronpot and Jack Taylor for critical review of this manuscript.


  • 1 The online version of this article contains supplemental material.

  • Address for reprint requests and other correspondence: R. S. Paules, 111 T. W. Alexander Dr., PO Box 12233, Mail Drop D2-03, Research Triangle Park, NC 27709 (e-mail: paules{at}niehs.nih.gov).

    Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).


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