Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries

被引:17
作者
Nikas, Jason B. [1 ,2 ]
Low, Walter C. [1 ,3 ,4 ]
机构
[1] Univ Minnesota, Sch Med, Dept Neurosurg, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Sch Med, Pharmaco Neuroimmunol Program, Minneapolis, MN 55455 USA
[3] Univ Minnesota, Sch Med, Grad Program Neurosci, Minneapolis, MN 55455 USA
[4] Univ Minnesota, Sch Med, Dept Integrat Biol & Physiol, Minneapolis, MN 55455 USA
基金
美国国家卫生研究院;
关键词
Diagnostic methods; Clustering analyses; K-means Clustering; Fuzzy Clustering; Medoid Partitioning Clustering; Hierarchical Clustering; Receiver operating characteristic (ROC) curve analysis; Nuclear magnetic resonance spectroscopy; Metabolomics; Huntington disease; PLURIPOTENT STEM-CELLS; SPECTROSCOPY; GENE;
D O I
10.1016/j.cmpb.2011.03.004
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Nuclear magnetic resonance (NMR) spectroscopy has emerged as a technology that can provide metabolite information within organ systems in vivo. In this study, we introduced a new method of employing a clustering algorithm to develop a diagnostic model that can differentially diagnose a single unknown subject in a disease with well-defined group boundaries. We used three tests to assess the suitability and the accuracy required for diagnostic purposes of the four clustering algorithms we investigated (K-means, Fuzzy, Hierarchical, and Medoid Partitioning). To accomplish this goal, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice and that of wild type (WT) mice using high field in vivo proton NMR spectroscopy (9.4 T). We tested all four clustering algorithms (1) with the original R6/2 HD mice and WT mice, (2) with unknown mice, whose status had been determined via genotyping, and (3) with the ability to separate the original R6/2 mice into the two age subgroups (8 and 12 weeks old). Only our diagnostic models that employed ROC-supervised Fuzzy, unsupervised Fuzzy, and ROC-supervised K-means Clustering passed all three stringent tests with 100% accuracy, indicating that they may be used for diagnostic purposes. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:E133 / E147
页数:15
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