The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A general framework for multiple testing dependence
Open accessAbstract
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.
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Cites
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Self-citationGenomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · cited 1×
“The second open problem is concerned with the fact that latent structure among the tests can distort what would usually be the correct null distribution of the test statistics (11, 25–27).”
BackgroundCapturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
Self-citationUse of Unlinked Genetic Markers to Detect Population Stratification in Association Studies
Pritchard, J. K., & Rosenberg, N. A. (1999) · The American Journal of Human Genetics · doi:10.1086/302449 · Cited by 1,198
Cited by
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
Self-citationHarmonization of multi-site diffusion tensor imaging data
Fortin, J.-P., Parker, D., Tunç, B., Watanabe, T., Elliott, M. A., Ruparel, K., Roalf, D. R., Satterthwaite, T. D., Gur, R. C., Gur, R. E., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.047 · Cited by 1,261
Landscape of X chromosome inactivation across human tissues
GTEx Consortium, Tukiainen, T., Villani, A.-C., Yen, A., Rivas, M. A., Marshall, J. L., Satija, R., Aguirre, M., Gauthier, L., Fleharty, M., et al. (2017) · Nature · doi:10.1038/nature24265 · Cited by 1,138
Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer
Teschendorff, A. E., Menon, U., Gentry-Maharaj, A., Ramus, S. J., Weisenberger, D. J., Shen, H., Campan, M., Noushmehr, H., Bell, C. G., Maxwell, A. P., et al. (2010) · Genome Research · doi:10.1101/gr.103606.109 · Cited by 855
Factors underlying variable DNA methylation in a human community cohort
Lam, L. L., Emberly, E., Fraser, H. B., Neumann, S. M., Chen, E., Miller, G. E., & Kobor, M. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121249109 · Cited by 476
Metadata
- Authors
- Leek, J. T., & Storey, J. D.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2008-12-02
- Volume
- 105
- Issue
- 48
- Pages
- 18718-18723
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2024103316
- Cited by
- 374
- References
- 6
- FWCI
- 14.59
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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