Hierarchical clustering of monoclonal antibody reactivity patterns in nonhuman species

Juan Pablo Pratt, Qing Zeng, Dino Ravnic, Harold Huss, James Rawn, Steven J. Mentzer

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)734-742
Number of pages9
JournalCytometry Part A
Volume75
Issue number9
DOIs
StatePublished - Sep 2009

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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