{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99418"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99418","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient visualization for large-scale and high-dimensional single-cell data","abstract":"This thesis is concerned with developing an efficient and scalable visualization method for large-scale and high-dimensional single-cell data. Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging. Thus, new methods to overcome high-dimensionality are necessary. Here, we present a computational tool that allows efficient visualization of high-dimensional single-cell data onto a low-dimensional (2D or 3D) space while preserving the similarity structure between single cells. We first construct a network that can represent the similarity structure between the high-dimensional representations of single cells, and then embed this network into a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.","abstract_html":"This thesis is concerned with developing an efficient and scalable visualization method for large-scale and high-dimensional single-cell data. Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging. Thus, new methods to overcome high-dimensionality are necessary. Here, we present a computational tool that allows efficient visualization of high-dimensional single-cell data onto a low-dimensional (2D or 3D) space while preserving the similarity structure between single cells. We first construct a network that can represent the similarity structure between the high-dimensional representations of single cells, and then embed this network into a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.","abstract_has_math":false,"creators":["Kim, Juho"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi","Peng, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:49:14Z","date_published":"2018-03-13T15:49:14Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Machine learning","Computational biology","Single-cell data analysis","Visualization","Dimensionality reduction","Network embedding"],"languages":["en"],"rights":["Copyright 2017 Juho Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99418","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi","Peng, Jian"]},{"key":"dc:creator","label":"Author","values":["Kim, Juho"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:49:14Z","2017-12-12","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Machine learning","Computational biology","Single-cell data analysis","Visualization","Dimensionality reduction","Network embedding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Juho Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99418"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis is concerned with developing an efficient and scalable visualization method for large-scale and high-dimensional single-cell data. Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging. Thus, new methods to overcome high-dimensionality are necessary. Here, we present a computational tool that allows efficient visualization of high-dimensional single-cell data onto a low-dimensional (2D or 3D) space while preserving the similarity structure between single cells. We first construct a network that can represent the similarity structure between the high-dimensional representations of single cells, and then embed this network into a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Juho Kim, accepted the attached license on 2017-12-11 at 17:04.","The student, Juho Kim, submitted this Thesis for approval on 2017-12-11 at 17:07.","This Thesis was approved for publication on 2017-12-12 at 08:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11946 on 2018-03-13 at 10:12:16","Made available in DSpace on 2018-03-13T15:49:14Z (GMT). No. of bitstreams: 2 KIM-THESIS-2017.pdf: 1680757 bytes, checksum: a12ec1db50868144182fb44e5062d906 (MD5) LICENSE.txt: 4205 bytes, checksum: 95b40df81d03724c54d2b77ddc8932a1 (MD5) Previous issue date: 2017-12-12"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Efficient visualization for large-scale and high-dimensional single-cell data"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Oluwasanmi","Peng, Jian"],"dc:creator":["Kim, Juho"],"dc:date":["2018-03-13T15:49:14Z","2017-12-12","2017-12"],"dc:description":["This thesis is concerned with developing an efficient and scalable visualization method for large-scale and high-dimensional single-cell data. Single-cell analysis can uncover the mysteries in the state of individual cells and enable us to construct new models of heterogeneous tissues. State-of-the-art technologies for single-cell analysis have been developed to measure the properties of single cells and detect hidden information. They are able to provide the measurements of dozens of features simultaneously in each cell. However, due to the high-dimensionality, heterogeneous complexity and sheer enormity of single-cell data, its interpretation is challenging. Thus, new methods to overcome high-dimensionality are necessary. Here, we present a computational tool that allows efficient visualization of high-dimensional single-cell data onto a low-dimensional (2D or 3D) space while preserving the similarity structure between single cells. We first construct a network that can represent the similarity structure between the high-dimensional representations of single cells, and then embed this network into a low-dimensional space through an efficient online optimization method based on the idea of negative sampling. Using this approach, we can preserve the high-dimensional structure of single-cell data in an embedded low-dimensional space that facilitates visual analyses of the data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Juho Kim, accepted the attached license on 2017-12-11 at 17:04.","The student, Juho Kim, submitted this Thesis for approval on 2017-12-11 at 17:07.","This Thesis was approved for publication on 2017-12-12 at 08:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11946 on 2018-03-13 at 10:12:16","Made available in DSpace on 2018-03-13T15:49:14Z (GMT). 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