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Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
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Tuan S Nguyen
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January 21, 2009
An important aspect of microarray studies involves the prediction of patient survival based on their gene expression levels. To cope with the high dimensionality of the microarray gene expression data, it is customary to first reduce the dimension of the gene expression data via dimension reduction methods, and then use the Cox proportional hazards model to predict patient survival. In this paper, we propose a variant of Partial Least Squares, denoted as Rank-based Modified Partial Least Squares (RMPLS), that is insensitive to outlying values of both the response and the gene expressions. We assess the performance of RMPLS and several dimension reduction methods using a simulation model for gene expression data with a censored response. In particular, Principal Component Analysis (PCA), modified Partial Least Squares (MPLS), RMPLS, Sliced Inverse Regression (SIR), Correlation Principal Component Regression (CPCR), Supervised Principal Component Regression (SPCR) and Univariate Selection (UNIV) are compared in terms of mean squared error of the estimated survival function and the estimated coefficients of the covariates, and in terms of the bias of the estimated survival function. It turns out that RMPLS outperforms all other methods in terms of the mean squared error and the bias of the survival function in the presence of outliers in the response. In addition, RMPLS is comparable to MPLS in the absence of outliers. In this setting, both RMPLS and MPLS outperform all other methods considered in this study in terms of mean squared error and bias of the estimated survival function.
Published Online: 2009-1-21
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
censored response;
Cox proportional hazards model;
outliers;
mean squared error;
bias
Articles in the same Issue
- Article
- Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
- Orthology-Based Multilevel Modeling of Differentially Expressed Mouse and Human Gene Pairs
- Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis
- Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
- A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins
- Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates
- A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms
- Hypothesis Tests for Point-Mass Mixture Data with Application to `Omics Data with Many Zero Values
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- Score Statistics for Mapping Quantitative Trait Loci
- Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements
- A Multilocus Model for Constructing a Linkage Disequilibrium Map in Human Populations
- Testing of Chromosomal Clumping of Gene Properties
- Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction
- Univariate Shrinkage in the Cox Model for High Dimensional Data
- Multilevel Comparison of Dendrograms: A New Method with an Application for Genetic Classifications
- Weighted Multiple Hypothesis Testing Procedures
- Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
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