False Discovery Rate Analysis in R. False discovery rate. False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons.
In a list of findings (i.e. studies where the null-hypotheses are rejected), FDR procedures are designed to control the expected proportion of incorrectly rejected null hypotheses ("false discoveries").[1] FDR controlling procedures exert a less stringent control over false discovery compared to familywise error rate (FWER) procedures (such as the Bonferroni correction), which seek to reduce the probability of even one false discovery, as opposed to the expected proportion of false discoveries. Thus FDR procedures have greater power at the cost of increased rates of type I errors, i.e., rejecting the null hypothesis of no effect when it should be accepted.[2]
R: Resampling-based multiple hypothesis testing. IBSB05F015.pdf (application/pdf Object) Multtest. Bioconductor version: Release (2.14) Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR).
Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). False Discovery Rate Analysis in R.