Hierarchical clustering of monoclonal antibody reactivity patterns in nonhuman species

Monoclonal antibodies are an important resource for defining molecular expression and probing molecular function. The characterization of monoclonal antibody reactivity patterns, however, can be costly and inefficient in nonhuman experimental systems. To develop a computational approach to the pattern analysis of monoclonal antibody reactivity, we analyzed a panel of 128 monoclonal antibodies recognizing sheep antigens. Quantitative single parameter flow cytometry histograms were obtained from five cell types isolated from normal animals. The resulting 640 histograms were smoothed using a Gaussian kernel over a range of bandwidths. Histogram features were selected by SiZer—an analytic tool that identifies statistically significant features. The extracted histogram features were compared and grouped using hierarchical clustering. The validity of the clustering was indicated by the accurate pairing of externally verified molecular reactivity. We conclude that our computational algorithm is a potentially useful tool for both monoclonal antibody classification and molecular taxonomy in nonhuman experimental systems.