Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37044
標題: A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues
作者: Rossin, E.
林宗儀
Lin, T.I.
Ho, H.J.
Mentzer, S.J.
Pyne, S.
關鍵字: flow-cytometric data
skew t-distribution
distributions
antigens
clusters
models
期刊/報告no:: Bioinformatics, Volume 27, Issue 19, Page(s) 2746-2753.
摘要: Motivation: Monoclonal antibodies (mAbs) are among the most powerful and important tools in biology and medicine. MAb development is of great significance to many research and clinical applications. Therefore, objective mAb classification is essential for categorizing and comparing mAb panels based on their reactivity patterns in different cellular species. However, typical flow cytometric mAb profiles present unique modeling challenges with their non-Gaussian features and intersample variations. It makes accurate mAb classification difficult to do with the currently used kernel-based or hierarchical clustering techniques. Results: To address these challenges, in the present study we developed a formal two-step framework called mAbprofiler for systematic, parametric characterization of mAb profiles. Further, we measured the reactivity of hundreds of new antibodies in diverse tissues using flow cytometry, which we successfully classified using mAbprofiler. First, mAbprofiler fits a mAb's flow cytometric histogram with a finite mixture model of skew t distributions that is robust against non-Gaussian features, and constructs a precise, smooth and mathematically rigorous profile. Then it performs novel curve clustering of the fitted mAb profiles using a skew t mixture of non-linear regression model that can handle intersample variation. Thus, mAbprofiler provides a new framework for identifying robust mAb classes, all well defined by distinct parametric templates, which can be used for classifying new mAb samples. We validated our classification results both computationally and empirically using mAb profiles of known classification.
URI: http://hdl.handle.net/11455/37044
ISSN: 1367-4803
文章連結: http://dx.doi.org/10.1093/bioinformatics/btr468
Appears in Collections:統計學研究所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.