title: Improve survival prediction using principal components of gene expression data.
abstract: The purpose of many microarray studies is to find the association between gene expression and sample characteristics such as treatment type or sample phenotype. There has been a surge of efforts developing different methods for delineating the association. Aside from the high dimensionality of microarray data, one well recognized challenge is the fact that genes could be complicatedly inter-related, thus making many statistical methods inappropriate to use directly on the expression data. Multivariate methods such as principal component analysis (PCA) and clustering are often used as a part of the effort to capture the gene correlation, and the derived components or clusters are used to describe the association between gene expression and sample phenotype. We propose a method for patient population dichotomization using maximally selected test statistics in combination with the PCA method, which shows favorable results. The proposed method is compared with a currently well-recognized method.
authors.affiliation: Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.
authors.forename: Yi Jing
authors.initials: YJ
authors.lastname: Shen
authors.forename: Shu Guang
authors.initials: SG
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D018572:Disease-Free Survival; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics* / Q000401:mortality; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010641:Phenotype; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve