Optimal alpha reduces error rates in gene expression studies: a meta-analysis approach

Background: Transcriptomic approaches (microarray and RNA-seq) have been a tremendous advance for molecular science in all disciplines, but they have made interpretation of hypothesis testing more difficult because of the large number of comparisons that are done within an experiment. The result has been a proliferation of techniques aimed at solving the multiple comparisons problem, techniques that have focused primarily on minimizing Type I error with little or no concern about concomitant increases in Type II errors. We have previously proposed a novel approach for setting statistical thresholds with applications for high throughput omics-data, optimal alpha, which minimizes the probability of making either error (i.e. Type I or II) and eliminates the need for post-hoc adjustments. Results: A meta-analysis of 242 microarray studies extracted from the peer-reviewed literature found that current practices for setting statistical thresholds led to very high Type II error rates. Further, we demonstrate that applying the optimal alpha approach results in error rates as low or lower than error rates obtained when using (i) no post-hoc adjustment, (ii) a Bonferroni adjustment and (iii) a false discovery rate (FDR) adjustment which is widely used in transcriptome studies. Conclusions: We conclude that optimal alpha can reduce error rates associated with transcripts in both microarray and RNA-seq experiments, but point out that improved statistical techniques alone cannot solve the problems associated with high throughput datasets - these approaches need to be coupled with improved experimental design that considers larger sample sizes and/or greater study replication.