Qvalue methods may not always control false discovery rate in genomic applications

The qvalue method by Storey (2002, 2003) has been proved to be theoretically sound for controlling false discovery rate in many high throughput genomic applications. However, empirical evidences suggest that this method can be more stringent than other methods, such as Bonferroni adjustment and the FDR method by Benjamini and Hochberg (1995). We compare these methods for detection of gene differential expression in microarray data analysis. For microarray experiment with the purpose of gene discovery, where many genes are expected to be differentially expressed across different experimental conditions, the qvalue method generally performs well. However, for experiments with only a few genes expected to be differentially expressed, the qvalue method performs much worse than other methods. Some insights are provided to examine this discrepancy. Adjustments to q-value method are recommended to accommodate many applications.