A general framework for multiple testing dependence

Jeffrey T. Leek, John D. Storey

Research output: Contribution to journalArticlepeer-review

248 Scopus citations


We develop a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We derive a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional dataset. This result shows a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We show theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology.

Original languageEnglish (US)
Pages (from-to)18718-18723
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number48
StatePublished - Dec 2 2008

All Science Journal Classification (ASJC) codes

  • General


  • Empirical null
  • False discovery rate
  • Latent structure
  • Simultaneous inference
  • Surrogate variable analysis


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