Between-group analysis of microarray data
Between-group analysis of microarray data
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DOI：
10.1093/bioinformatics/18.12.1600
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发表时间：
2002-12-01
期刊：
影响因子：
5.8
通讯作者：
Higgins, DG
中科院分区：
文献类型：
--
作者：
Culhane, AC;Perrière, G;Higgins, DG
动机：大多数监督分类方法受到案例多于变量要求的限制。在微阵列数据中，变量（基因）的数量远远超过案例（阵列）的数量，因此需要对基因进行过滤和预选择。我们描述了组间分析（BGA）在微阵列数据分析中的应用。BGA的一个特点是，当变量（基因）的数量超过案例（数组）的数量时，可以使用它。BGA基于使用对应分析（COA）等标准方法对样品组进行排序，而不是对单个微阵列样品进行排序。因此，可以将其视为对分组数据执行COA的一种方法。结果：我们使用两个癌症数据集说明了该方法的功能。在这两种情况下，我们都可以快速准确地从任意数量的指定先验组中分类测试样本，并识别这些组的特征基因。我们获得了非常高的正确分类率，这是通过训练集和测试集的刀切或验证实验确定的。结果在准确性方面与其他方法相当，但BGA的功能和灵活性使其成为分析微阵列癌症数据的特别有吸引力的方法。
Motivation: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data.Results: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.