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Hierarchical clustering of monoclonal antibody reactivity patterns in nonhuman species
Author(s) -
Pratt Juan Pablo,
Zeng Qing,
Ravnic Dino,
Huss Harold,
Rawn James,
Mentzer Steven J.
Publication year - 2009
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.20768
Subject(s) - histogram , monoclonal antibody , cluster analysis , hierarchical clustering , computational biology , pattern recognition (psychology) , computer science , biology , artificial intelligence , antibody , genetics , image (mathematics)
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. © 2009 International Society for Advancement of Cytometry