Computationally efficient permutation-based confidence interval estimation for tail-area FDR.

ABSTRACT: Challenges of satisfying parametric assumptions in genomic settings with thousands or millions of tests have led investigators to combine powerful False Discovery Rate (FDR) approaches with computationally expensive but exact permutation testing. We describe a computationally efficient permutation-based approach that includes a tractable estimator of the proportion of true null hypotheses, the variance of the log of tail-area FDR, and a confidence interval (CI) estimator, which accounts for the number of permutations conducted and dependencies between tests. The CI estimator applies a binomial distribution and an overdispersion parameter to counts of positive tests. The approach is general with regards to the distribution of the test statistic, it performs favorably in comparison to other

SUBMITTER: Millstein J 

PROVIDER: S-EPMC3775454 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature