Bayesian variable selection for the analysis of microarray data with censored outcomes.
BACKGROUND
A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions.


RESULTS
We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers.


BACKGROUND
The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author.


BACKGROUND
mvannucci@stat.tamu.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Sha
Naijun
N
Department of Mathematical Sciences, University of Texas at El Paso El Paso, TX 79968, USA.
Tadesse
Mahlet G
MG
Vannucci
Marina
M
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods