{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115458"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115458","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Statistical learning approaches for obtaining interpretable reduced representations of multimodal sequencing datasets","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Leistico, Jacob R"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Song, Jun S","Gruebele, Martin","Kahn, Yonatan","Kuehn, Seppe"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Epigenetic","multimodal","tensor decomposition","single-cell"],"languages":["en","eng"],"rights":["Copyright 2022 Jacob Leistico"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115458","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Song, Jun S","Gruebele, Martin","Kahn, Yonatan","Kuehn, Seppe"]},{"key":"dc:creator","label":"Author","values":["Leistico, Jacob R"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-19"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Epigenetic","multimodal","tensor decomposition","single-cell"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Jacob Leistico"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115458"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Jacob Leistico, accepted the attached license on 2022-04-14 at 13:02.","The student, Jacob Leistico, submitted this Dissertation for approval on 2022-04-14 at 13:26.","This Dissertation was approved for publication on 2022-04-19 at 11:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17672 on 2022-11-11 at 13:15:16","The highly specialized functions of cells in a multicellular organism are effected by the proteins contained within and produced by the cell. Establishing the distinct protein composition of specialized cell types is accomplished largely through the regulation of gene transcription. Several epigenetic mechanisms, including chemical modifications of histone proteins, collectively function to establish and maintain the transcriptional state necessary for the specialized functions of the cell. The collection of RNA transcript abundance, protein content, and epigenetic features create a multimodal description of cell identity. Advances in high throughput sequencing technologies are increasingly enabling the profiling of multiple cellular modalities in bulk tissues and single cells. Computational algorithms that can integrate information from the multiple profiled modalities are needed to fully utilize the resulting information in classifying tissue or cell types and identifying the salient molecular features. This thesis presents rigorous computational methods for integrating multimodal genomic data to extract biologically relevant features that can help identify tissue or cell types. These methods share the common approach of obtaining reduced representations of the multimodal sequencing datasets that can be used for downstream analysis. The first method applies a higher order singular value decomposition (HOSVD) to decompose an epigenetic data tensor obtained from profiling multiple histone modifications in human tissue samples. The reduced representations obtained with this method are shown to capture features differentiating disease conditions, mutational subtypes, and tissue types. Additionally these representations are connected to covarying profiles in genomic location space that are shown to identify recurrent epigenetic differences between disease conditions. The second method applies the spectral clustering on multilayer graphs (SCML) algorithm to cluster cells from a multimodal single-cell sequencing dataset that simultaneously profiles mRNA transcripts and selected surface proteins. Clusters obtained with the SCML algorithm are shown to compromise between the community structure obtained from the mRNA transcripts and the protein surface markers. The results presented in this thesis demonstrate the benefits of integrating multiple molecular modalities in identifying cell and tissue types."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Statistical learning approaches for obtaining interpretable reduced representations of multimodal sequencing datasets"]}]}],"canonical_facts":{"dc:contributor":["Song, Jun S","Gruebele, Martin","Kahn, Yonatan","Kuehn, Seppe"],"dc:creator":["Leistico, Jacob R"],"dc:date":["2022-05","2022-04-19"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Jacob Leistico, accepted the attached license on 2022-04-14 at 13:02.","The student, Jacob Leistico, submitted this Dissertation for approval on 2022-04-14 at 13:26.","This Dissertation was approved for publication on 2022-04-19 at 11:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17672 on 2022-11-11 at 13:15:16","The highly specialized functions of cells in a multicellular organism are effected by the proteins contained within and produced by the cell. Establishing the distinct protein composition of specialized cell types is accomplished largely through the regulation of gene transcription. Several epigenetic mechanisms, including chemical modifications of histone proteins, collectively function to establish and maintain the transcriptional state necessary for the specialized functions of the cell. The collection of RNA transcript abundance, protein content, and epigenetic features create a multimodal description of cell identity. Advances in high throughput sequencing technologies are increasingly enabling the profiling of multiple cellular modalities in bulk tissues and single cells. Computational algorithms that can integrate information from the multiple profiled modalities are needed to fully utilize the resulting information in classifying tissue or cell types and identifying the salient molecular features. This thesis presents rigorous computational methods for integrating multimodal genomic data to extract biologically relevant features that can help identify tissue or cell types. These methods share the common approach of obtaining reduced representations of the multimodal sequencing datasets that can be used for downstream analysis. The first method applies a higher order singular value decomposition (HOSVD) to decompose an epigenetic data tensor obtained from profiling multiple histone modifications in human tissue samples. The reduced representations obtained with this method are shown to capture features differentiating disease conditions, mutational subtypes, and tissue types. Additionally these representations are connected to covarying profiles in genomic location space that are shown to identify recurrent epigenetic differences between disease conditions. The second method applies the spectral clustering on multilayer graphs (SCML) algorithm to cluster cells from a multimodal single-cell sequencing dataset that simultaneously profiles mRNA transcripts and selected surface proteins. Clusters obtained with the SCML algorithm are shown to compromise between the community structure obtained from the mRNA transcripts and the protein surface markers. The results presented in this thesis demonstrate the benefits of integrating multiple molecular modalities in identifying cell and tissue types."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115458"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jacob Leistico"],"dc:subject":["Epigenetic","multimodal","tensor decomposition","single-cell"],"dc:title":["Statistical learning approaches for obtaining interpretable reduced representations of multimodal sequencing datasets"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}