False Discovery Rate (FDR)

Definition

The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. It is typically used in high-throughput experiments in order to correct for random events that falsely appear significant. When testing a null hypothesis to determine whether an observed score is statistically significant, a measure of confidence, the p-value, is calculated and compared to a confidence threshold α. When k hypotheses are tested simultaneously with a confidence level α, the chances of occurrence of false positives (i.e., rejecting the null hypothesis when in fact it is true) is equal to 1 − (1 − α)^k, which can lead to a high error rate in the experiment. Therefore, a multiple testing correction, such as the FDR, is needed to adjust our statistical confidence measures based on the number of tests performed.

The FDR is defined as the expected proportion of false discoveries, i.e., incorrectly rejected null hypothesis, among all...