EAMA: Empirically adjusted meta-analysis for large-scale simultaneous hypothesis testing in genomic experiments.

ABSTRACT: Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. However, adhering to the regular statistical assumptions regarding the null distributions of test statistics in such large-scale multiple testing frameworks has the potential of leading to incorrect significance testing results and biased inference. This problem gets worse when one combines results from different independent genomic experiments with a possibility of ending up with gross false discoveries of significant genes. In this article, we develop a meta-analysis method of combining p-values from different independent experiments involving large-scale multiple testing frameworks, through empirical adjustments of the individual test statistics

SUBMITTER: Sikdar S 

PROVIDER: S-EPMC5663489 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature