{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108457"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108457","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Comparative analysis of methods for microbiome study","abstract":"This Thesis was approved for publication on 2020-07-08 at 09:48.","abstract_html":"This Thesis was approved for publication on 2020-07-08 at 09:48.","abstract_has_math":false,"creators":["Iyer, Mihir Vishwanath"],"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":["Iyer, Ravishankar K"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T20:59:39Z","date_published":"2020-10-07T20:59:39Z","updated_at":"2026-07-22T22:24:48Z","subjects":["microbiome","data science"],"languages":["en"],"rights":["Copyright 2020 Mihir Iyer"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108457","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K"]},{"key":"dc:creator","label":"Author","values":["Iyer, Mihir Vishwanath"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T20:59:39Z","2020-07-08","2020-08"]},{"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":["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":["microbiome","data science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Mihir Iyer"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108457"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Thesis was approved for publication on 2020-07-08 at 09:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15514 on 2020-10-02 at 15:12:16","Made available in DSpace on 2020-10-07T20:59:39Z (GMT). No. of bitstreams: 2 IYER-THESIS-2020.pdf: 2060237 bytes, checksum: 6db11ac757d8b0d0922c64509fbe5a76 (MD5) LICENSE.txt: 4207 bytes, checksum: 088907f14309e91af43e7ab36b086423 (MD5) Previous issue date: 2020-07-08","Microbiome analysis is garnering much interest with benefits including improved treatment options, enhanced capabilities for personalized medicine, greater understanding of the human body, and contributions to ecological study. Data from these communities of bacteria, viruses, and fungi are feature rich, sparse, and have sample sizes not appreciably larger than the feature space, making analysis challenging and necessitating a coordinated approach utilizing multiple techniques alongside domain expertise. This thesis provides an overview and comparative analysis of these methods, with a case study on cirrhosis and hepatic encephalopathy demonstrating a selection of methods. Approaches are considered in a medically motivated context where relationships between microbes in the human body and diseases or conditions are of primary interest, with additional objectives being the identification of how microbes influence each other and how these influences relate to the diseases and conditions being studied. These analysis methods are partitioned into three categories: univariate statistical methods, classifier-based methods, and joint analysis methods. Univariate statistical methods provide results corresponding to how much a single variable or feature differs between groups in the data. Classifier-based approaches can be generalized as those where a classification model with microbe abundance as inputs and disease states as outputs is used, resulting in a predictive model which is then analyzed to learn about the data. The joint analysis category corresponds to techniques which specifically target relationships between microbes and compare those relationships among subpopulations within the data. Despite significant differences between these categories and the individual methods, each has strengths and weaknesses and plays an important role in microbiome analysis.","Submission original under an indefinite embargo labeled 'Open Access'. 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Approaches are considered in a medically motivated context where relationships between microbes in the human body and diseases or conditions are of primary interest, with additional objectives being the identification of how microbes influence each other and how these influences relate to the diseases and conditions being studied. These analysis methods are partitioned into three categories: univariate statistical methods, classifier-based methods, and joint analysis methods. Univariate statistical methods provide results corresponding to how much a single variable or feature differs between groups in the data. Classifier-based approaches can be generalized as those where a classification model with microbe abundance as inputs and disease states as outputs is used, resulting in a predictive model which is then analyzed to learn about the data. The joint analysis category corresponds to techniques which specifically target relationships between microbes and compare those relationships among subpopulations within the data. Despite significant differences between these categories and the individual methods, each has strengths and weaknesses and plays an important role in microbiome analysis.","Submission original under an indefinite embargo labeled 'Open Access'. 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