{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/100986"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/100986","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multimodal data analysis applied to a medical setting","abstract":"Complex diseases, such as cancer, have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. It is also important to capture the interaction between different types of cells, an important indicator of disease status. To that end, it is crucial to quantify and utilize the spatial distribution of various cell types within the examined tissue at different scales. We employ Ripley's K-statistic, a traditional feature employed in geographical information systems, which captures spatial distribution patterns of individual point sets and interactions between multiple point sets. We propose to improve the histopathology image features by incorporating this descriptor to capture the spatial distribution of the cells, and interactions between lymphocytes and epithelial cells. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of 0.736 (p approx 1e-14) and 0.471, (p approx 7e-3) for CCA and Sparse CCA, respectively, of several image features with expression of PAM50 genes, known to be linked to outcome. Sparse CCA, an extension of CCA based on sparsity, revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. The utility of the Ripley's K-statistic on 710 TCGA breast invasive carcinoma (BRCA) patients' histopathology images in the context of imaging-genetics is demonstrated by its superior correlations with gene expressions. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer, and the importance of capturing spatial features at multiple scales.","abstract_html":"Complex diseases, such as cancer, have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. It is also important to capture the interaction between different types of cells, an important indicator of disease status. To that end, it is crucial to quantify and utilize the spatial distribution of various cell types within the examined tissue at different scales. We employ Ripley's K-statistic, a traditional feature employed in geographical information systems, which captures spatial distribution patterns of individual point sets and interactions between multiple point sets. We propose to improve the histopathology image features by incorporating this descriptor to capture the spatial distribution of the cells, and interactions between lymphocytes and epithelial cells. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of 0.736 (p approx 1e-14) and 0.471, (p approx 7e-3) for CCA and Sparse CCA, respectively, of several image features with expression of PAM50 genes, known to be linked to outcome. Sparse CCA, an extension of CCA based on sparsity, revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. The utility of the Ripley's K-statistic on 710 TCGA breast invasive carcinoma (BRCA) patients' histopathology images in the context of imaging-genetics is demonstrated by its superior correlations with gene expressions. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer, and the importance of capturing spatial features at multiple scales.","abstract_has_math":false,"creators":["Vaishnavi Subramanian, -"],"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":["Do, Minh N."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:27:09Z","date_published":"2018-09-04T20:27:09Z","updated_at":"2026-07-22T22:24:38Z","subjects":["histopathology","image","multimodal","data","spatial","correlation","CCA","medical","genes","TCGA","cancer","sparsity"],"languages":["en"],"rights":["Copyright 2018 - Vaishnavi Subramanian"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/100986","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N."]},{"key":"dc:creator","label":"Author","values":["Vaishnavi Subramanian, -"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:27:09Z","2018-04-16","2018-05"]},{"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":["histopathology","image","multimodal","data","spatial","correlation","CCA","medical","genes","TCGA","cancer","sparsity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 - Vaishnavi Subramanian"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/100986"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Complex diseases, such as cancer, have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. It is also important to capture the interaction between different types of cells, an important indicator of disease status. To that end, it is crucial to quantify and utilize the spatial distribution of various cell types within the examined tissue at different scales. We employ Ripley's K-statistic, a traditional feature employed in geographical information systems, which captures spatial distribution patterns of individual point sets and interactions between multiple point sets. We propose to improve the histopathology image features by incorporating this descriptor to capture the spatial distribution of the cells, and interactions between lymphocytes and epithelial cells. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of 0.736 (p approx 1e-14) and 0.471, (p approx 7e-3) for CCA and Sparse CCA, respectively, of several image features with expression of PAM50 genes, known to be linked to outcome. Sparse CCA, an extension of CCA based on sparsity, revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. The utility of the Ripley's K-statistic on 710 TCGA breast invasive carcinoma (BRCA) patients' histopathology images in the context of imaging-genetics is demonstrated by its superior correlations with gene expressions. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer, and the importance of capturing spatial features at multiple scales.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, - Vaishnavi Subramanian, accepted the attached license on 2018-04-16 at 11:00.","The student, - Vaishnavi Subramanian, submitted this Thesis for approval on 2018-04-16 at 11:01.","This Thesis was approved for publication on 2018-04-16 at 13:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12270 on 2018-08-31 at 17:12:45","Made available in DSpace on 2018-09-04T20:27:09Z (GMT). No. of bitstreams: 2 VAISHNAVISUBRAMANIAN-THESIS-2018.pdf: 13003590 bytes, checksum: dd0233a743d9aa54277480e05f4ddd5d (MD5) LICENSE.txt: 4220 bytes, checksum: 024faa0ca77565d0b5314d1892dc854b (MD5) Previous issue date: 2018-04-16"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multimodal data analysis applied to a medical setting"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh N."],"dc:creator":["Vaishnavi Subramanian, -"],"dc:date":["2018-09-04T20:27:09Z","2018-04-16","2018-05"],"dc:description":["Complex diseases, such as cancer, have traditionally been studied using genetic data, or images alone. To understand the biology of such diseases, joint analysis of multiple data modalities could provide interesting insights. We propose the use of canonical correlation analysis (CCA) as a preliminary discovery tool for identifying connections across modalities, specifically between gene expression and features describing cell and nucleus shape, texture, and stain intensity in histopathological images. It is also important to capture the interaction between different types of cells, an important indicator of disease status. To that end, it is crucial to quantify and utilize the spatial distribution of various cell types within the examined tissue at different scales. We employ Ripley's K-statistic, a traditional feature employed in geographical information systems, which captures spatial distribution patterns of individual point sets and interactions between multiple point sets. We propose to improve the histopathology image features by incorporating this descriptor to capture the spatial distribution of the cells, and interactions between lymphocytes and epithelial cells. Applied to 615 breast cancer samples from The Cancer Genome Atlas, CCA revealed significant correlation of 0.736 (p approx 1e-14) and 0.471, (p approx 7e-3) for CCA and Sparse CCA, respectively, of several image features with expression of PAM50 genes, known to be linked to outcome. Sparse CCA, an extension of CCA based on sparsity, revealed associations with enrichment of pathways implicated in cancer without leveraging prior biological understanding. The utility of the Ripley's K-statistic on 710 TCGA breast invasive carcinoma (BRCA) patients' histopathology images in the context of imaging-genetics is demonstrated by its superior correlations with gene expressions. These findings affirm the utility of CCA for joint phenotype-genotype analysis of cancer, and the importance of capturing spatial features at multiple scales.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, - Vaishnavi Subramanian, accepted the attached license on 2018-04-16 at 11:00.","The student, - Vaishnavi Subramanian, submitted this Thesis for approval on 2018-04-16 at 11:01.","This Thesis was approved for publication on 2018-04-16 at 13:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12270 on 2018-08-31 at 17:12:45","Made available in DSpace on 2018-09-04T20:27:09Z (GMT). No. of bitstreams: 2 VAISHNAVISUBRAMANIAN-THESIS-2018.pdf: 13003590 bytes, checksum: dd0233a743d9aa54277480e05f4ddd5d (MD5) LICENSE.txt: 4220 bytes, checksum: 024faa0ca77565d0b5314d1892dc854b (MD5) Previous issue date: 2018-04-16"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/100986"],"dc:language":["en"],"dc:rights":["Copyright 2018 - Vaishnavi Subramanian"],"dc:subject":["histopathology","image","multimodal","data","spatial","correlation","CCA","medical","genes","TCGA","cancer","sparsity"],"dc:title":["Multimodal data analysis applied to a medical setting"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:38Z"}