{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116075"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116075","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Single-cell multi-omic data analysis with mathematical and statistical methods","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Zhang, Shuyi"],"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","Golding, Ido","Kim, Sangjin","Zhao, Sihai Dave"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["Sequencing analysis","Stochastic processes","Information geometry","Spectral graph theory"],"languages":["en","eng"],"rights":["Copyright 2022 Shuyi Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116075","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Song, Jun S","Golding, Ido","Kim, Sangjin","Zhao, Sihai Dave"]},{"key":"dc:creator","label":"Author","values":["Zhang, Shuyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-13"]},{"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":["Sequencing analysis","Stochastic processes","Information geometry","Spectral graph theory"]}]},{"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 Shuyi Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116075"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Shuyi Zhang, accepted the attached license on 2022-07-11 at 11:12.","The student, Shuyi Zhang, submitted this Dissertation for approval on 2022-07-11 at 11:31.","This Dissertation was approved for publication on 2022-07-13 at 17:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18212 on 2022-11-15 at 19:17:32","Recent advances in next-generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. From the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology, in particular, count data of surface proteins allow for immunophenotyping of cells yet pose new computational challenges; there is currently a dearth of rigorous mathematical tools for analyzing the data. In this thesis, we seek to address three issues in data analysis for CITE-seq, namely, removing the systematic biases between samples, calling true signals from noise, and merging information from multiple modalities. First, we utilize concepts and ideas from Riemannian geometry to remove batch effects between samples. Subsequently, we develop a framework for distinguishing positive signals from background noise using statistical inference and multiple testing. Lastly, we use the ideas of Hamiltonian operators and density matrices from physics and introduce a unified graph-based learning scheme for effectively merging information from multiple modalities. The strengths of these approaches are demonstrated on CITE-seq data sets of mouse and human tissue samples. The geometrical methods for batch correction, the statistical methods for signal detection, and the graph-based methods for effectively merging the multiple modalities that we introduce in this thesis provide promising frameworks based on ideas from mathematics, statistics, and physics for analyzing the multi-omic data generated using the CITE-seq technology."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Single-cell multi-omic data analysis with mathematical and statistical methods"]}]}],"canonical_facts":{"dc:contributor":["Song, Jun S","Golding, Ido","Kim, Sangjin","Zhao, Sihai Dave"],"dc:creator":["Zhang, Shuyi"],"dc:date":["2022-08","2022-07-13"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01","The student, Shuyi Zhang, accepted the attached license on 2022-07-11 at 11:12.","The student, Shuyi Zhang, submitted this Dissertation for approval on 2022-07-11 at 11:31.","This Dissertation was approved for publication on 2022-07-13 at 17:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18212 on 2022-11-15 at 19:17:32","Recent advances in next-generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. From the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology, in particular, count data of surface proteins allow for immunophenotyping of cells yet pose new computational challenges; there is currently a dearth of rigorous mathematical tools for analyzing the data. In this thesis, we seek to address three issues in data analysis for CITE-seq, namely, removing the systematic biases between samples, calling true signals from noise, and merging information from multiple modalities. First, we utilize concepts and ideas from Riemannian geometry to remove batch effects between samples. Subsequently, we develop a framework for distinguishing positive signals from background noise using statistical inference and multiple testing. Lastly, we use the ideas of Hamiltonian operators and density matrices from physics and introduce a unified graph-based learning scheme for effectively merging information from multiple modalities. The strengths of these approaches are demonstrated on CITE-seq data sets of mouse and human tissue samples. The geometrical methods for batch correction, the statistical methods for signal detection, and the graph-based methods for effectively merging the multiple modalities that we introduce in this thesis provide promising frameworks based on ideas from mathematics, statistics, and physics for analyzing the multi-omic data generated using the CITE-seq technology."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116075"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Shuyi Zhang"],"dc:subject":["Sequencing analysis","Stochastic processes","Information geometry","Spectral graph theory"],"dc:title":["Single-cell multi-omic data analysis with mathematical and statistical methods"],"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":"2026-07-22T22:24:55Z"}