Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays

References

• Aryee, M., Jaffe, A., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A., Hansen, K., and Irizarry, R. (2014), “Minfi: A Flexible and Comprehensive Bioconductor Package for the Analysis of Infinium DNA Methylation Microarrays,” Bioinformatics, 30, 1363–1369.
   PubMed Web of Science ®Google Scholar
• Becker, C., Hagmann, J., Müller, J., Koenig, D., Stegle, O., Borgwardt, K., and Weigel, D. (2011), “Spontaneous Epigenetic Variation in the Arabidopsis Thaliana Methylome,” Nature, 480, 245–249.
   PubMed Web of Science ®Google Scholar
• Benjamini, Y., and Speed, T. P. (2012), “Summarizing and Correcting the GC Content Bias in High-Throughput Sequencing,” Nucleic Acids Research, 40, e72.
   PubMed Web of Science ®Google Scholar
• Benjamini, Y., and Yekutieli, D. (2005), “False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters,” Journal of the American Statistical Association, 100, 71–81.
   Web of Science ®Google Scholar
• Berk, R., Brown, L., Buja, A., Zhang, K., and Zhao, L., et al. (2013), “Valid Post-Selection Inference,” The Annals of Statistics, 41, 802–837.
   Web of Science ®Google Scholar
• Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J. M., Delano, D., Zhang, L., Schroth, G. P., Gunderson, K. L., Fan, J., and Shen, R. (2011), “High Density DNA Methylation Array with Single CpG Site Resolution,” Genomics, 98, 288–295.
   PubMed Web of Science ®Google Scholar
• Bock, C., Walter, J., Paulsen, M., and Lengauer, T. (2008), “Inter-Individual Variation of DNA Methylation and its Implications for Large-Scale Epigenome Mapping,” Nucleic Acids Research, 36, e55.
   PubMed Web of Science ®Google Scholar
• Cai, T. T., and Yuan, M. (2014), “Rate-Optimal Detection of Very Short Signal Segments,” arXiv:1407.2812.
   Google Scholar
• DiCiccio, T. J., and Romano, J. P. (1988), “On Parametric Bootstrap Procedures for Second-Order Accurate Confidence Limits,” Technical Report No. 293, Stanford University.
   Google Scholar
• Efron, B. (1985), “Bootstrap Confidence Intervals for a Class of Parametric Problems,” Biometrika, 72, 45–58.
   Web of Science ®Google Scholar
• Eklund, A., Nichols, T. E., and Knutsson, H. (2016), “Cluster Failure: Why fMRI Inferences for Spatial Extent have Inflated False-Positive Rates,” Proceedings of the National Academy of Sciences of the United States of America, 113, 7900–7905.
   PubMed Web of Science ®Google Scholar
• Feinberg, A. P., and Tycko, B. (2004), “The History of Cancer Epigenetics,” Nature Reviews Cancer, 4, 143–53.
   PubMed Web of Science ®Google Scholar
• Fithian, W., Sun, D., and Taylor, J. (2014), “Optimal Inference After Model Selection,” arXiv:1407.2812.
   Google Scholar
• Friston, K. J., Worsley, K. J., Frackowiak, R., Mazziotta, J. C., and Evans, A. C. (1994), “Assessing the Significance of Focal Activations using their Spatial Extent,” Human Brain Mapping, 1, 210–220.
   PubMedGoogle Scholar
• Hagler, D. J., Saygin, A. P., and Sereno, M. I. (2006), “Smoothing and Cluster Thresholding for Cortical Surface-Based Group Analysis of fMRI Data,” Neuroimage, 33, 1093–1103.
   PubMed Web of Science ®Google Scholar
• Hansen, K., Langmead, B., and Irizarry, R. (2012), “BSmooth: From Whole Genome Bisulfite Sequencing Reads to Differentially Methylated Regions,” Genome Biology, 13, R83.
   PubMed Web of Science ®Google Scholar
• Hayasaka, S., and Nichols, T. E. (2003), “Validating Cluster Size Inference: Random Field and Permutation Methods,” NeuroImage, 20, 2343–2356.
   PubMed Web of Science ®Google Scholar
• Hayasaka, S., Phan, K., Liberzon, I., Worsley, K. J., and Nichols, T. E. (2004), “Nonstationary Cluster-Size Inference with Random Field And Permutation Methods,” NeuroImage, 22, 676–687.
   PubMed Web of Science ®Google Scholar
• Horrace, W. C. (2005), “Some Results on the Multivariate Truncated Normal Distribution,” Journal of Multivariate Analysis, 94, 209–221.
   Web of Science ®Google Scholar
• Jaenisch, R., and Bird, A. (2003), “Epigenetic Regulation of Gene Expression: How the Genome Integrates Intrinsic and Environmental Signals,” Nature Genetics, 33, 245–254.
   PubMed Web of Science ®Google Scholar
• Jaffe, A. E., Feinberg, A. P., Irizarry, R. A., and Leek, J. T. (2012a), “Significance Analysis and Statistical Dissection of Variably Methylated Regions,” Biostatistics, 13, 166–178.
   PubMed Web of Science ®Google Scholar
• Jaffe, A. E., Murakami, P., Lee, H., Leek, J. T., Fallin, M. D., Feinberg, A. P., and Irizarry, R. A. (2012b), “Bump Hunting to Identify Differentially Methylated Regions in Epigenetic Epidemiology Studies,” International Journal of Epidemiology, 41, 200–209.
   PubMed Web of Science ®Google Scholar
• Knijnenburg, T. A., Ramsey, S. A., Berman, B. P., Kennedy, K. A., Smit, A. F. A., Wessels, L. F. A., Laird, P. W., Aderem, A., and Shmulevich, I. (2014), “Multiscale Representation of Genomic Signals,” Nature Methods, 11, 689–694.
   PubMed Web of Science ®Google Scholar
• Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., and Baker, C. I. (2009), “Circular Analysis in Systems Neuroscience: The Dangers of Double Dipping,” Nature Neuroscience, 12, 535–540.
   PubMed Web of Science ®Google Scholar
• Kuan, P. F., and Chiang, D. Y. (2012), “Integrating Prior Knowledge in Multiple Testing under Dependence with Applications to Detecting Differential DNA Methylation,” Biometrics, 68, 774–783.
   PubMed Web of Science ®Google Scholar
• Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M. J., et al. (2015), “Integrative Analysis of 111 Reference Human Epigenomes,” Nature, 518, 317–330.
   PubMed Web of Science ®Google Scholar
• Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E. (2016), “Exact Post-Selection Inference, with Application to the Lasso,” The Annals of Statistics, 44, 907–927.
   Web of Science ®Google Scholar
• Lee, L.-F. (1981), “Consistent Estimation of a Multivariate Doubly Truncated or Censored Tobit Model,” Discussion Paper No. 153, Center for Economic Research, Department of Economics, University of Minnesota.
   Google Scholar
• Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., and Irizarry, R. A. (2010), “Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data,” Nature Reviews Genetics, 11, 733–739.
   PubMed Web of Science ®Google Scholar
• Lehmann, E. L., and Romano, J. P. (2005), Testing Statistical Hypotheses (3rd ed.), New York: Springer.
   Google Scholar
• Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., Lucero, J., Huang, Y., Dwork, A. J., Schultz, M. D., Yu, M., Tonti-Filippini, J., Heyn, H., Hu, S., Wu, J. C., Rao, A., Esteller, M., He, C., Haghighi, F. G., Sejnowski, T., J., Behrens, M. M., and Ecker, J. R. (2013), “Global Epigenomic Reconfiguration During Mammalian Brain Development,” Science, 341, 1237905.
   PubMed Web of Science ®Google Scholar
• Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., Edsall, L., Antosiewicz-Bourget, J., Stewart, R., Ruotti, V., Millar, A. H., Thomson, J. A., Ren, B., and Ecker, J. R. (2009), “Human DNA Methylomes at Base Resolution Show Widespread Epigenomic Differences,” Nature, 462, 315–322.
   PubMed Web of Science ®Google Scholar
• Lockhart, R., Taylor, J., Tibshirani, R. J., and Tibshirani, R. (2014), “A Significance Test for the Lasso,” The Annals of Statistics, 42, 413–468.
   PubMed Web of Science ®Google Scholar
• Pacis, A., Tailleux, L., Morin, A. M., Lambourne, J., MacIsaac, J. L., Yotova, V., Dumaine, A., Danckaert, A., Luca, F., Grenier, J.-C., Hansen, K. D., Gicquel, B., Yu, M., Pai, A., He, C., Tung, J., Pastinen, T., Kobor, M. S., Pique-Regi, R., Gilad, Y., and Barreiro, L. B. (2015), “Bacterial Infection Remodels the DNA Methylation Landscape of Human Dendritic Cells,” Genome Research, 25, pp. 1801–1811.
   PubMed Web of Science ®Google Scholar
• Pakman, A., and Paninski, L. (2014), “Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians,” Journal of Computational and Graphical Statistics, 23, 518–542.
   Web of Science ®Google Scholar
• Pedersen, B. S., Schwartz, D. A., Yang, I. V., and Kechris, K. J. (2012), “Comb-p: Software for Combining, Analyzing, Grouping and Correcting Spatially Correlated P-Values,” Bioinformatics, 28, 2986–2988.
   PubMed Web of Science ®Google Scholar
• Razin, A., and Riggs, A. D. (1980), “DNA Methylation and Gene Function,” Science, 210, 604–610.
   PubMed Web of Science ®Google Scholar
• Rinott, Y., and Scarsini, M. (2006), “Total Positivity Order and the Normal Distribution,” Journal of Multivariate Analysis, 97, 1251–1261.
   Web of Science ®Google Scholar
• Robertson, K. D. (2005), “DNA Methylation and Human Disease,” Nature Reviews Genetics, 6, 597–610.
   PubMed Web of Science ®Google Scholar
• Schwartzman, A., Gavrilov, Y., and Adler, R. J. (2011), “Multiple Testing of Local Maxima for Detection of Peaks in 1D,” The Annals of Statistics, 39, 3290–3319.
   PubMed Web of Science ®Google Scholar
• Schwartzman, A., Jaffe, A., Gavrilov, Y., and Meyer, C. A. (2013), “Multiple Testing of Local Maxima for Detection of Peaks in ChIP-Seq Data,” The Annals of Applied Statistics, 7, 471–494.
   PubMed Web of Science ®Google Scholar
• Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., Lundin, P., Månér, S., Massa, H., Walker, M., Chi, M., Navin, N., Lucito, R., Healy, J., Hicks, J., Ye, K., Reiner, A., Gilliam, T. C., Trask, B., Patterson, N., Zetterberg, A., and Wigler, M. (2004), “Large-Scale Copy Number Polymorphism in the Human Genome,” Science, 305, 525–528.
   PubMed Web of Science ®Google Scholar
• Siegmund, D., Yakir, B., and Zhang, N. (2011), “The False Discovery Rate for Scan Statistics,” Biometrika, 98, 979–985.
   Web of Science ®Google Scholar
• Sommerfeld, M., Sain, S., and Schwartzman, A. (2017), “Confidence Regions for Spatial Excursion Sets From Repeated Random Field Observations, With an Application to Climate,” Journal of the American Statistical Association, 113, 1328–1341.
   Web of Science ®Google Scholar
• Song, L., and Crawford, G. E. (2010), “DNase-seq: A High-Resolution Technique for Mapping Active Gene Regulatory Elements Across the Genome from Mammalian Cells,” Cold Spring Harbor Protocols, 2010, pdb–prot5384.
   PubMedGoogle Scholar
• Sun, W., Reich, B. J., Tony Cai, T., Guindani, M., and Schwartzman, A. (2015), “False Discovery Control in Large-Scale Spatial Multiple Testing,” Journal of the Royal Statistical Society, Series B, 77, 59–83.
   Google Scholar
• Weinstein, A., Fithian, W., and Benjamini, Y. (2013), “Selection Adjusted Confidence Intervals with More Power to Determine the Sign,” Journal of the American Statistical Association, 108, 165–176.
   Web of Science ®Google Scholar
• Woo, C.-W., Krishnan, A., and Wager, T. D. (2014), “Cluster-Extent based Thresholding in fMRI Analyses: Pitfalls and Recommendations,” Neuroimage, 91, 412–419.
   PubMed Web of Science ®Google Scholar
• Zhang, N. R., and Siegmund, D. O. (2012), “Model Selection for High-Dimensional, Multi-Sequence Change-Point Problems,” Statistica Sinica, 22, 1507–1538.
   Web of Science ®Google Scholar
• Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., and Liu, X. S. (2008), “Model-Based Analysis of ChIP-Seq (MACS),” Genome biology, 9, Article R137.
   Web of Science ®Google Scholar
• Zhong, H., and Prentice, R. L. (2008), “Bias-Reduced Estimators and Confidence Intervals for Odds Ratios in Genome-Wide Association Studies,” Biostatistics, 9, 621–634.
   PubMed Web of Science ®Google Scholar