Ghosh Debashis
Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA.
Pac Symp Biocomput. 2002:18-29.
An important problem in the analysis of microarray data is correlating the high-dimensional measurements with clinical phenotypes. In this paper, we develop predictive models for associating gene expression data from microarray experiments with such outcomes. They are based on the singular value decomposition. We propose new algorithms for performing gene selection and gene clustering based on these predictive models. The estimation procedure using the regression models occurs in two stages. First, the gene expression measurements are transformed using the singular value decomposition. The regression parameters in the model linking the principal components with the clinical responses are then estimated using maximum likelihood. We demonstrate the application of the methodology to data from a breast cancer study.
微阵列数据分析中的一个重要问题是将高维测量结果与临床表型相关联。在本文中，我们开发了预测模型，用于将微阵列实验中的基因表达数据与此类结果相关联。这些模型基于奇异值分解。我们提出了基于这些预测模型进行基因选择和基因聚类的新算法。使用回归模型的估计过程分两个阶段进行。首先，使用奇异值分解对基因表达测量值进行变换。然后使用最大似然估计模型中连接主成分与临床反应的回归参数。我们展示了该方法在乳腺癌研究数据中的应用。
