{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115567"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115567","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine leanring algorithms for single-cell data analysis","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Peng, Jianhao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Milenkovic, Olgica","Ochoa, Idoia","Raginsky, Maxim","Shormonoy, Ilan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["single cell","online algorithm","network analysis","gene regulatory network"],"languages":["en","eng"],"rights":["Copyright 2022 Jianhao Peng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115567","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Milenkovic, Olgica","Ochoa, Idoia","Raginsky, Maxim","Shormonoy, Ilan"]},{"key":"dc:creator","label":"Author","values":["Peng, Jianhao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-22"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["single cell","online algorithm","network analysis","gene regulatory network"]}]},{"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 Jianhao Peng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115567"]}]},{"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 2024-05-01","The student, Jianhao Peng, accepted the attached license on 2022-04-18 at 13:10.","The student, Jianhao Peng, submitted this Dissertation for approval on 2022-04-18 at 13:19.","This Dissertation was approved for publication on 2022-04-22 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17751 on 2022-11-11 at 12:06:01","In this thesis, we proposed various machine learning algorithms for analyzing different types of single cell sequencing data. Starting with the most common single cell RNA-seq data in Chapter 2, we proposed an online convex matrix factorization algorithm named online cvxMF that can efficiently learn representatives and interpretable lower-dimension basis vectors for each cell type. In Chapter 3, we introduced ChIA-Drop, a new type of network-structured data for chromatin interaction analysis, and extended our online cvxMF algorithm to a novel online convex network dictionary learning method that includes MCMC sampling and Gene Ontology enrichment analysis. The newly proposed method, online cvxNDL, is able to accurately reconstruct the original ChIA-Drop network and provide network dictionaries associated with biological functions. Lastly in Chapter 4, we proposed SimiC, a single cell gene regulatory network (GRN) inference algorithm that can jointly learn several GRNs from related cell phenotypes. Combined with regulon activity scores and regulatory dissimilarity scores for each of the driver genes across different phenotypes, SimiC is able to capture regulatory dynamics that are missed by previous methods."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine leanring algorithms for single-cell data analysis"]}]}],"canonical_facts":{"dc:contributor":["Milenkovic, Olgica","Ochoa, Idoia","Raginsky, Maxim","Shormonoy, Ilan"],"dc:creator":["Peng, Jianhao"],"dc:date":["2022-05","2022-04-22"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Jianhao Peng, accepted the attached license on 2022-04-18 at 13:10.","The student, Jianhao Peng, submitted this Dissertation for approval on 2022-04-18 at 13:19.","This Dissertation was approved for publication on 2022-04-22 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17751 on 2022-11-11 at 12:06:01","In this thesis, we proposed various machine learning algorithms for analyzing different types of single cell sequencing data. Starting with the most common single cell RNA-seq data in Chapter 2, we proposed an online convex matrix factorization algorithm named online cvxMF that can efficiently learn representatives and interpretable lower-dimension basis vectors for each cell type. In Chapter 3, we introduced ChIA-Drop, a new type of network-structured data for chromatin interaction analysis, and extended our online cvxMF algorithm to a novel online convex network dictionary learning method that includes MCMC sampling and Gene Ontology enrichment analysis. The newly proposed method, online cvxNDL, is able to accurately reconstruct the original ChIA-Drop network and provide network dictionaries associated with biological functions. Lastly in Chapter 4, we proposed SimiC, a single cell gene regulatory network (GRN) inference algorithm that can jointly learn several GRNs from related cell phenotypes. Combined with regulon activity scores and regulatory dissimilarity scores for each of the driver genes across different phenotypes, SimiC is able to capture regulatory dynamics that are missed by previous methods."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115567"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jianhao Peng"],"dc:subject":["single cell","online algorithm","network analysis","gene regulatory network"],"dc:title":["Machine leanring algorithms for single-cell data analysis"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"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"}