Classification of microarrays to nearest centroids
Classification of microarrays to nearest centroids
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DOI：
10.1093/bioinformatics/bti681
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发表时间：
2005-11-15
期刊：
影响因子：
5.8
通讯作者：
Dabney, AR
中科院分区：
文献类型：
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作者：
Dabney, AR
动机：通过微阵列对生物样品进行分类是一个非常有趣的话题。已经提出了许多方法，并成功地应用于这个问题。最近的研究表明，最近的质心分类提供了一个准确的预测，可能优于更复杂的方法。“微阵列预测分析”（PAM）方法就是这样一个例子，作者强烈鼓励其简单性和可解释性。本着这种精神，我试图评估的分类器的性能比甚至PAM。结果：我令人惊讶地表明，修改后的t-统计和收缩的质心采用PAM往往会增加误分类错误时，与他们的简单的同行。基于这些观察，我提出了一种分类方法，称为“分类到最近的质心”（ClaNC）。ClaNC通过标准t统计对基因进行排名，不收缩质心，并使用类别特异性基因选择程序。由于这些修改，ClaNC可以说比PAM更简单，更容易解释，它可以被看作是一个传统的最近的质心分类器，使用特别选择的基因。我证明，CLANC错误率往往是显着低于PAM，对于给定数量的活性基因。
Motivation: Classification of biological samples by microarrays is a topic of much interest. A number of methods have been proposed and successfully applied to this problem. It has recently been shown that classification by nearest centroids provides an accurate predictor that may outperform much more complicated methods. The 'Prediction Analysis of Microarrays' (PAM) approach is one such example, which the authors strongly motivate by its simplicity and interpretability. In this spirit, I seek to assess the performance of classifiers simpler than even PAM.Results: I surprisingly show that the modified t-statistics and shrunken centroids employed by PAM tend to increase misclassification error when compared with their simpler counterparts. Based on these observations, I propose a classification method called 'Classification to Nearest Centroids' (ClaNC). ClaNC ranks genes by standard t-statistics, does not shrink centroids and uses a class-specific gene-selection procedure. Because of these modifications, ClaNC is arguably simpler and easier to interpret than PAM, and it can be viewed as a traditional nearest centroid classifier that uses specially selected genes. I demonstrate that ClaNC error rates tend to be significantly less than those for PAM, for a given number of active genes.