Bioinformatics Advance Access originally published online on July 15, 2004
Bioinformatics 2004 20(18):3406-3412; doi:10.1093/bioinformatics/bth415
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Bioinformatics vol. 20 issue 18 © Oxford University Press 2004; all rights reserved.
Dimension reduction methods for microarrays with application to censored survival data
1 Department of Biochemistry and Molecular Medicine and 2 Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA
Received on June 2, 2004; accepted on July 7, 2004
Advance Access Publication July 15, 2004
Motivation: Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles.
Results: We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis.
Availability: R programs are available upon request from the authors.
Supplementary information: http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.
Contact: lexli{at}ucdavis.edu
* To whom correspondence should be addressed.
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