Simultaneous statistical inference for epigenetic data

PLoS ONE

Volumn 10, Issue 5, 2015, Pages

  Schildknecht, Konstantin ^a   Olek, Sven ^b   Dickhaus, Thorsten ^c  

^a WEIERSTRASS INSTITUTE FOR APPLIED ANALYSIS AND STOCHASTICS   (Germany)

^b EPIONTIS GMBH   (Germany)

^c UNIVERSITY OF BREMEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL ERROR; ARTICLE; COMPARATIVE STUDY; COMPUTER SIMULATION; EPIGENETICS; GENE LOCUS; GENETIC ANALYSIS; MATHEMATICAL ANALYSIS; METHODOLOGY; MULTIVARIATE ANALYSIS; MUTATIONAL ANALYSIS; NONPARAMETRIC TEST; NULL HYPOTHESIS; RELIABILITY; STATISTICAL ANALYSIS; BIOLOGY; GENETIC DATABASE; GENETIC EPIGENESIS; HUMAN; PROCEDURES; STATISTICAL DISTRIBUTION;

COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; EPIGENESIS, GENETIC; HUMANS; STATISTICAL DISTRIBUTIONS;

EID: 84930684493     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0125587     Document Type: Article

References (32)

1. Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nature Reviews Genetics 9: 465-476. doi: 10.1038/nrg2341 PMID: 18463664
2. Jaffe AE, Feinberg AP, Irizarry RA, Leek JT (2012) Significance analysis and statistical dissection of variably methylated regions. Biostatistics 13: 166-178. doi: 10.1093/biostatistics/kxr013 PMID: 21685414
3. Wieczorek G, Asemissen A, Model F, Türbachova I, Floess S, Liebenberg V, et al. (2009) Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue. Cancer Res 69: 599-608. doi: 10.1158/0008-5472.CAN-08-2361 PMID: 19147574
4. Sehouli J, Loddenkemper C, Cornu T, Schwachula T, Hoffmüller U, Grützkau A, et al. (2011) Epigenetic quantification of tumor-infiltrating T-lymphocytes. Epigenetics 6: 236-246. doi: 10.4161/epi.6.2.13755 PMID: 20962591
5. Laird PW (2010) Principles and challenges of genome-wide DNA methylation analysis. Nature Reviews Genetics 11: 191-203. doi: 10.1038/nrg2732 PMID: 20125086
6. Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, et al. (2001) Linkage disequilibrium in the human genome. Nature 411: 199-204. doi: 10.1038/35075590 PMID: 11346797
7. Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, Burger M, et al. (2006) DNA methylation profiling of human chromosomes 6, 20 and 22. Nature Genetics 38: 1378-1385. doi: 10.1038/ng1909 PMID: 17072317
8. Baron U, Türbachova I, Hellwag A, Eckhardt F, Berlin K, Hoffmüller U, et al. (2006) DNA methylation analysis as a tool for cell typing. Epigenetics 1: 55-60. doi: 10.4161/epi.1.1.2643 PMID: 17998806
9. Dickhaus T (2014) Simultaneous statistical inference. With applications in the life sciences. Berlin: Springer.
10. Chen Z, Liu Q, Nadarajah S (2012) A new statistical approach to detecting differentially methylated loci for case control Illumina array methylation data. Bioinformatics 28: 1109-1113. doi: 10.1093/bioinformatics/bts093 PMID: 22368244
11. Chen Z, Huang H, Liu J, Ng HKT, Nadarajah S, Huang X, et al. (2013) Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data. BMC Med Genomics 6 Suppl 1: S9. doi: 10.1186/1755-8794-6-S1-S9 PMID: 23369576
12. Siegmund KD, Laird PW (2002) Analysis of complex methylation data. Methods 27: 170-178. doi: 10.1016/S1046-2023(02)00071-3 PMID: 12095277
13. Huang H, Chen Z, Huang X (2013) Age-adjusted nonparametric detection of differential DNA methylation with case-control designs. BMC Bioinformatics 14: 86. doi: 10.1186/1471-2105-14-86 PMID: 23497201
14. Chen Z, Huang H, Liu Q (2014) Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data. BMC Bioinformatics 15: 142. doi: 10.1186/1471-2105-15-142 PMID: 24884464
15. Houseman EA, Christensen BC, Yeh RF, Marsit CJ, Karagas MR, Wrensch M, et al. (2008) Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics 9: 365. doi: 10.1186/1471-2105-9-365 PMID: 18782434
16. Jelizarow M, Cieza A, Mansmann U (2014) Global permutation tests for multivariate ordinal data: alternatives, test statistics and the null dilemma. Journal of the Royal Statistical Society C forthcoming.
17. Brunner E, Munzel U (2013) Nichtparametrische Datenanalyse. Unverbundene Stichproben. Heidelberg: Springer Spektrum, 2nd updated and corrected edition.
18. Ruymgaart F (1980). A unified approach to the asymptotic distribution theory of certain midrank statistics. Statistique non parametrique asymptotique, Actes Journ. statist., Rouen/France 1979, Lect. Notes Math. 821, 1-18 (1980).
19. Sugiura N (1965) Multisample and multivariate nonparametric tests based on U statistics and their asymptotic efficiencies. Osaka Journal of Mathematics 2: 385-426.
20. Brunner E, Munzel U, Puri ML (2002) The multivariate nonparametric Behrens-Fisher problem. Journal of Statistical Planning and Inference 108: 37-53. doi: 10.1016/S0378-3758(02)00269-0
21. Marcus R, Peritz E, Gabriel K (1976) On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63: 655-660. doi: 10.1093/biomet/63.3.655
22. Chung EY, Romano JP (2013) Multivariate and multiple permutation tests. Technical report, Technical Report 2013-05, Dept. Statistics, Stanford University.
23. Chung EY, Romano JP (2011) Asymptotically valid and exact permutation tests based on two-sample U-statistics. Technical report, Technical Report 2011-09, Dept. Statistics, Stanford University.
24. Neubert K, Brunner E (2007) A Studentized permutation test for the non-parametric Behrens-Fisher problem. Comput Stat Data Anal 51: 5192-5204. doi: 10.1016/j.csda.2006.05.024
25. Pauly M, Asendorf T, Konietschke F (2014) Permutation tests and confidence intervals for the area under the ROC-Curve. Technical report, Universität Ulm. URL http://www.uni-ulm.de/fileadmin/website-uni-ulm/mawi.inst.105/Publikationen/FrankThomas-8.pdf.
26. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, et al. (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20: 440-446. doi: 10.1101/gr.103606.109 PMID: 20219944
27. Dickhaus T, Strassburger K, Schunk D, Morcillo-Suarez C, Illig T, Navarro A (2012) How to analyze many contingency tables simultaneously in genetic association studies. Stat Appl Genet Mol Biol 11: Article 12 PMID: 22850061
28. Natsuka S, Gersten KM, Zenita K, Kannagi R, Lowe JB (1994) Molecular cloning of a cDNA encoding a novel human leukocyte alpha-1,3-fucosyltransferase capable of synthesizing the sialyl Lewis x determinant. J Biol Chem 269: 16789-16794. PMID: 8207002
29. Türbachova I, Schwachula T, Vasconcelos I, Mustea A, Baldinger T, Jones KA, et al. (2013) The cellular ratio of immune tolerance (immunoCRIT) is a definite marker for aggressiveness of solid tumors and may explain tumor dissemination patterns. Epigenetics 8: 1226-1235. doi: 10.4161/epi.26334 PMID: 24071829
30. Pauly M, Brunner E, Konietschke F (2014) Asymptotic permutation tests in general factorial designs. Journal of the Royal Statistical Society: Series B (Statistical Methodology) online first,
31. Romano JP, Wolf M (2005) Exact and approximate stepdown methods for multiple hypothesis testing. J Am Stat Assoc 100: 94-108. doi: 10.1198/016214504000000539
32. Finner H, Dickhaus T (2010) Edgeworth expansions and rates of convergence for normalized sums: Chung's 1946 method revisited. Stat Probab Lett 80: 1875-1880. doi: 10.1016/j.spl.2010.08.014