title: Averaged gene expressions for regression.
abstract: 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.
authors.affiliation: Google Inc, Mountain View, CA 94043, USA. meeyoung@google.com
authors.forename: Mee Young
authors.initials: MY
authors.lastname: Park
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis