Combining multiple microarray studies using bootstrap meta-analysis

Microarray technology has enabled us to simultaneously measure the expression of thousands of genes. Using this high-throughput data collection, we can examine subtle genetic changes between biological samples and build predictive models for clinical applications. Although microarrays have dramatically increased the rate of data collection, sample size is still a major issue in feature selection. Previous methods show that microarray data combination is successful in improving selection when using z-scores and fold change. We propose a wrapper based gene selection technique that combines bootstrap estimated classification errors for individual genes across multiple datasets. The bootstrap is an unbiased estimator of classification error and has been shown to be effective for small sample data. Coupled with data combination across multiple data sets, we show that this meta-analytic approach improves gene selection.