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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭朱杏(Chuhsing Kate Hsiao) | |
| dc.contributor.author | Yen-Chen Feng | en |
| dc.contributor.author | 馮嬿臻 | zh_TW |
| dc.date.accessioned | 2021-06-16T23:37:22Z | - |
| dc.date.available | 2013-07-01 | |
| dc.date.copyright | 2012-09-17 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-26 | |
| dc.identifier.citation | Ando, T. (2007). Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika, 94(2), 443-458.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., . . . Consortium, G. O. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. Chen, Z., Liu, Q., & Nadarajah, S. (2012). A new statistical approach to detecting differentially methylated loci for case control Illumina array methylation data. [Research Support, N.I.H., Extramural]. Bioinformatics, 28(8), 1109-1113. Das, P. M., & Singal, R. (2004). DNA methylation and cancer. Journal of Clinical Oncology, 22(22), 4632-4642. Down, T. A., Rakyan, V. K., Turner, D. J., Flicek, P., Li, H., Kulesha, E., . . . Beck, S. (2008). A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. [Research Support, Non-U.S. Gov't]. Nat Biotechnol, 26(7), 779-785. Esteller, M. (2007). Cancer epigenomics: DNA methylomes and histone-modification maps. Nature Reviews Genetics, 8(4), 286-298. Esteller, M. (2008). Molecular origins of cancer: Epigenetics in cancer. New England Journal of Medicine, 358(11), 1148-1159. Fang, Y. C., Lai, P. T., Dai, H. J., & Hsu, W. L. (2011). MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature. Bmc Bioinformatics, 12(1), 471. Feinberg, A. P., & Irizarry, R. A. (2010). Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proceedings of the National Academy of Sciences of the United States of America, 107, 1757-1764. Gardiner-Garden, M., & Frommer, M. (1987). CpG islands in vertebrate genomes. Journal of molecular biology, 196(2), 261. Hosack, D. A., Dennis, G., Sherman, B. T., Lane, H. C., & Lempicki, R. A. (2003). Identifying biological themes within lists of genes with EASE. Genome Biology, 4(10). Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13. Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44-57. Irizarry, R. A., Ladd-Acosta, C., Carvalho, B., Wu, H., Brandenburg, S. A., Jeddeloh, J. A., . . . Feinberg, A. P. (2008). Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Research, 18(5), 780-790. Irizarry, R. A., Ladd-Acosta, C., Wen, B., Wu, Z. J., Montano, C., Onyango, P., . . . Feinberg, A. P. (2009). The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nature Genetics, 41(2), 178-186. Jaffe, A. E., Feinberg, A. P., Irizarry, R. A., & Leek, J. T. (2012). Significance analysis and statistical dissection of variably methylated regions. Biostatistics, 13(1), 166-178. Jeong, J., Li, L., Liu, Y., Nephew, K. P., Huang, T. H., & Shen, C. (2010). An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer. [Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.]. BMC Med Genomics, 3, 55. Kuan, P. F., & Chiang, D. Y. (2012). Integrating Prior Knowledge in Multiple Testing under Dependence with Applications to Detecting Differential DNA Methylation. Biometrics. Laird, P. W. (2010). Principles and challenges of genome-wide DNA methylation analysis. Nature Reviews Genetics, 11(3), 191-203. Laurila, K., Oster, B., Andersen, C. L., Lamy, P., Orntoft, T., Yli-Harja, O., & Wiuf, C. (2011). A Beta-mixture model for dimensionality reduction, sample classification and analysis. Bmc Bioinformatics, 12. McCabe, M. T., Brandes, J. C., & Vertino, P. M. (2009). Cancer DNA Methylation: Molecular Mechanisms and Clinical Implications. Clinical Cancer Research, 15(12), 3927-3937. Rakyan, V. K., Down, T. A., Maslau, S., Andrew, T., Yang, T. P., Beyan, H., . . . Spector, T. D. (2010). Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Research, 20(4), 434-439. Rein, B. J. D., Gupta, S., Dada, R., Safi, J., Michener, C., & Agarwal, A. (2011). Potential markers for detection and monitoring of ovarian cancer. Journal of Oncology, 2011. Siegmund, K. D. (2011). Statistical approaches for the analysis of DNA methylation microarray data. Human Genetics, 129(6), 585-595. Teschendorff, A. E., Menon, U., Gentry-Maharaj, A., Ramus, S. J., Weisenberger, D. J., Shen, H., . . . Widschwendter, M. (2010). Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Research, 20(4), 440-446. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65339 | - |
| dc.description.abstract | DNA methylation is known to be associated with cancer susceptibility. Such biochemical process, however, is also affected by other factors such as age, tissues, nutrition and other environmental variates. In other words, the methylation pattern can vary greatly between and within individuals. An appropriate study design for DNA methylation, therefore, should be able to control these sources of variation. Because most current case-control studies for identification of differentially methylated CpG sites may not be able to account for this heterogeneity, we propose in the present study for matched cases and controls a Bayesian hierarchical model with specially designed priors for CpG sites locating in different areas. This model can accommodate the individual heterogeneity in methylation data and allows the CpG sites to express non-exchangeable patterns. The analysis showed that this model can incorporate more biological interpretation with two different types of prior distributions considered for CpG islands and non-CpG enriched regions, respectively. The United Kingdom Ovarian Cancer Population Study (UKOPS) was used for illustration; methylation data from the study was generated by Illumina Infinium BeadArray technology. Parameters were estimated by Markov chain Monte Carlo (MCMC) method using OpenBUGS software package. The hyperparameter λ is of interest to measure methylation difference between case and age-matched control at each specific CpG. Probability of λ>0 in posterior samples was calculated for each CpG locus; 0.70, 0.90, 0.95, and 0.99 cut-off points of Pr (λ_i>0) resulted in 7877, 1068, 421, and 90 potential hypermethylated CpGs, respectively. A gene ontology analysis showed that 398 genes of hypermethylated CpGs in the 0.95 cutoff group were enriched in functions associated with carcinogenesis, including programmed cell death, positive regulation of cell cycle,
and immune cell activation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T23:37:22Z (GMT). No. of bitstreams: 1 ntu-101-R00849004-1.pdf: 1479633 bytes, checksum: 5a5ca4e18809641b707cbe0de4c960ed (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 1. Introduction 1
2. Methods 3 2.1 Bayesian hierarchical model 3 2.2 Formulation of priors on 흀풊 4 2.2.1 Correlations for CpG loci 4 2.2.2 Prior specification on hyper-parameters α_i and β_i 6 3. Results 8 4. Discussion 11 References 14 Lists of Figures and Tables Figure 1. DNA methylation patterns are altered in cancer cells 16 Figure 2. Aging-associated CpG locus 17 Figure 3. Hierarchical model illustration 18 Figure 4. Methylation distribution before and after quantile normalization 19 Figure 5. Locations and correlation plots of blocks on chromosome 21 20 Figure 6. Correlation plots of ordered CpGs based on CGI status 21 Figure 7. OpenBUGS output of chromosome 21 22 Table 1. Information on the 8 blocks of chromosome 21 23 Table 2. Sensitivity analysis for 흀풊 24 Table 3. Number of CpGs and genes under different cutoff Pr (λ_i>0) 24 Table 4. Gene ontology analysis 25 Table 5. Gene methylation and ovarian cancer 26 | |
| dc.language.iso | en | |
| dc.subject | CpG位點之先驗知識 | zh_TW |
| dc.subject | CpG島之高度甲基化 | zh_TW |
| dc.subject | 貝氏統計方法 | zh_TW |
| dc.subject | 年齡配對之病例對照設計 | zh_TW |
| dc.subject | prior knowledge for CpG location | en |
| dc.subject | CpG island hypermethylation | en |
| dc.subject | Bayesian approach | en |
| dc.subject | age matching | en |
| dc.title | 以貝氏階層模型對人類基因體DNA高度甲基化的情形進行機率推論 | zh_TW |
| dc.title | Bayesian Inference of DNA Hypermethylation Based on Global Methylation Profiling | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳為堅(Wei J. Chen),郭柏秀(Po-Hsiu Kuo),陳保中(Pau-Chung Chen) | |
| dc.subject.keyword | CpG島之高度甲基化,貝氏統計方法,年齡配對之病例對照設計,CpG位點之先驗知識, | zh_TW |
| dc.subject.keyword | CpG island hypermethylation,Bayesian approach,age matching,prior knowledge for CpG location, | en |
| dc.relation.page | 26 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-07-26 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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