Pratt Juan Pablo, Zeng Qing, Ravnic Dino, Huss Harold, Rawn James, Mentzer Steven J
Laboratory of Immunophysiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
Cytometry A. 2009 Sep;75(9):734-42. doi: 10.1002/cyto.a.20768.
Monoclonal antibodies (Mab) are an important resource for defining molecular expression and probing molecular function. The characterization of Mab reactivity patterns, however, can be costly and inefficient in nonhuman experimental systems. To develop a computational approach to the pattern analysis of Mab reactivity, we analyzed a panel of 128 Mab 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 Mab classification and molecular taxonomy in nonhuman experimental systems.
单克隆抗体（Mab）是定义分子表达和探究分子功能的重要资源。然而，在非人类实验系统中，表征Mab反应模式可能成本高昂且效率低下。为了开发一种用于Mab反应模式分析的计算方法，我们分析了一组识别绵羊抗原的128种Mab。从正常动物分离的五种细胞类型中获得了定量单参数流式细胞术直方图。使用高斯核在一系列带宽范围内对所得的640个直方图进行平滑处理。通过SiZer（一种识别具有统计学意义特征的分析工具）选择直方图特征。使用层次聚类对提取的直方图特征进行比较和分组。外部验证的分子反应性的准确配对表明了聚类的有效性。我们得出结论，我们的计算算法对于非人类实验系统中的Mab分类和分子分类学而言是一种潜在有用的工具。
