{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-1155"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-1155","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"Inferential Methods for High-Throughput Methylation Data","abstract":"The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.","abstract_html":"The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.","abstract_has_math":false,"creators":["Capparuccini, Maria"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"Dissertation","degree_discipline":"Biostatistics","degree_department":null,"school":null,"contributors":["Kellie Archer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-11-23T08:00:00Z","date_published":"2010-11-23T08:00:00Z","updated_at":"2026-07-24T05:53:26Z","subjects":["High-Throughput technology","DNA Methylation","Mixed Modelling","Beta Test","Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/156"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/156","href":"https://scholarscompass.vcu.edu/etd/156","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/QE0E-H643","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kellie Archer"]},{"key":"dc:creator","label":"Author","values":["Capparuccini, Maria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-12-15T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biostatistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["High-Throughput technology","DNA Methylation","Mixed Modelling","Beta Test","Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© The Author"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.25772/QE0E-H643","https://scholarscompass.vcu.edu/etd/156"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well."]},{"key":"dc:title","label":"Title","values":["Inferential Methods for High-Throughput Methylation Data"]}]}],"canonical_facts":{"dc:contributor":["Kellie Archer"],"dc:creator":["Capparuccini, Maria"],"dc:date.available":["2015-12-15T08:00:00Z"],"dc:description.abstract":["The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well."],"dc:identifier":["https://doi.org/10.25772/QE0E-H643","https://scholarscompass.vcu.edu/etd/156"],"dc:rights":["© The Author"],"dc:subject":["High-Throughput technology","DNA Methylation","Mixed Modelling","Beta Test","Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"dc:title":["Inferential Methods for High-Throughput Methylation Data"],"thesis:degree_discipline":["Biostatistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T05:53:26Z"}