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Abstract:
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Recent high-throughput technology developments have enabled simultaneous analysis of numerous genes in a single experiment. For meta-analysis of such experiments, development of novel approaches is crucial since the sample sizes of individual experiments are generally small compared to the number of genes being tested. In such large-scale multiple testing frameworks, adhering to the regular statistical assumptions regarding the null distributions of test statistics can potentially lead to incorrect significance testing results and biased inference. This problem gets worse when one combines large-scale multiple testing p-values from different independent genomic experiments in presence of some hidden confounders. I will show how empirical adjustments of the individual test statistics and p-values from different independent experiments outperform the standard meta-analysis approaches of significance testing, especially in potential presence of hidden confounders, by accurately identifying the truly significant set of genes in simulation studies and real genomic datasets.
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