Gaussian Regularized Sliced Inverse Regression
Statistics and Computing
Survival prediction using gene expression data: A review and comparison
Computational Statistics & Data Analysis
Advances in seeded dimension reduction: Bootstrap criteria and extensions
Computational Statistics & Data Analysis
| Hi-index | 3.84 |
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.