{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-3597"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-3597","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"A functional data analytic approach for region level differential DNA methylation detection","abstract":"\"DNA methylation is an epigenetic modification that can alter gene expression without a DNA sequence change. The role of DNA methylation in biological processes and human health is important to understand, with many studies identifying associations between specific methylation patterns and diseases such as cancer. In mammals, DNA methylation almost always occurs when a methyl group attaches to a cytosine followed by a guanine (i.e. CpG dinucleotides) on the DNA sequence. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of these novel approaches are compared with an alternative method (M3D) for region level testing\"--Abstract, page iv.","abstract_html":""DNA methylation is an epigenetic modification that can alter gene expression without a DNA sequence change. The role of DNA methylation in biological processes and human health is important to understand, with many studies identifying associations between specific methylation patterns and diseases such as cancer. In mammals, DNA methylation almost always occurs when a methyl group attaches to a cytosine followed by a guanine (i.e. CpG dinucleotides) on the DNA sequence. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of these novel approaches are compared with an alternative method (M3D) for region level testing"--Abstract, page iv.","abstract_has_math":false,"creators":["Milad, Mohamed Salem F."],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Mathematics","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:43Z","subjects":["Bioinformatics","Biostatistics","Statistics and Probability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/2592","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Milad, Mohamed Salem F."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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In mammals, DNA methylation almost always occurs when a methyl group attaches to a cytosine followed by a guanine (i.e. CpG dinucleotides) on the DNA sequence. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of these novel approaches are compared with an alternative method (M3D) for region level testing\"--Abstract, page iv."]},{"key":"dc:title","label":"Title","values":["A functional data analytic approach for region level differential DNA methylation detection"]}]}],"canonical_facts":{"dc:creator":["Milad, Mohamed Salem F."],"dc:description.abstract":["\"DNA methylation is an epigenetic modification that can alter gene expression without a DNA sequence change. The role of DNA methylation in biological processes and human health is important to understand, with many studies identifying associations between specific methylation patterns and diseases such as cancer. In mammals, DNA methylation almost always occurs when a methyl group attaches to a cytosine followed by a guanine (i.e. CpG dinucleotides) on the DNA sequence. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of these novel approaches are compared with an alternative method (M3D) for region level testing\"--Abstract, page iv."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/2592"],"dc:subject":["Bioinformatics","Biostatistics","Statistics and Probability"],"dc:title":["A functional data analytic approach for region level differential DNA methylation detection"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. 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