Averaged gene expressions for regression

被引:109
作者
Park, Mee Young
Hastie, Trevor
Tibshirani, Robert
机构
[1] Google Inc, Mountain View, CA 94043 USA
[2] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Hlth Res & Policy, Stanford, CA 94305 USA
关键词
averaging; hierarchical clustering; Lasso; variance reduction;
D O I
10.1093/biostatistics/kxl002
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Although averaging is a simple technique, it plays an important role in reducing variance. We use this essential property of averaging in regression of the DNA microarray data, which poses the challenge of having far more features than samples. In this paper, we introduce a two-step procedure that combines (1) hierarchical clustering and (2) Lasso. By averaging the genes within the clusters obtained from hierarchical clustering, we define supergenes and use them to fit regression models, thereby attaining concise interpretation and accuracy. Our methods are supported with theoretical justifications and demonstrated on simulated and real data sets.
引用
收藏
页码:212 / 227
页数:16
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