Statistical Applications in Genetics and Molecular Biology PLS Dimension Reduction for Classification with Microarray Data
Statistical Applications in Genetics and Molecular Biology PLS Dimension Reduction for Classification with Microarray Data
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发表时间：
2011
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通讯作者：
A. Boulesteix
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作者：
A. Boulesteix
偏最小二乘法（PLS）降维在使用高维微阵列数据进行分类的情况下已知具有良好的预测准确性。在本文中，将由PLS降维和对新成分进行线性判别分析组成的分类过程与一些最先进的分类方法进行了比较。此外，将一种提升算法应用于该分类方法。另外，提出了一种选择PLS成分数量的简单方法。研究了PLS降维和基因选择之间的联系，并证明了用于二分类的第一个PLS成分的一个性质。此外，我们展示了如何使用真实数据将PLS用于数据可视化。整个研究基于9个真实的微阵列癌症数据集。
Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting algorithm is applied to this classification method. In addition, a simple procedure to choose the number of PLS components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proved. In addition, we show how PLS can be used for data visualization using real data. The whole study is based on 9 real microarray cancer data sets.