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Machine Learning Methods for the Analysis of Multi-Modal Spatial Omics Data
Machine Learning Methods for the Analysis of Multi-Modal Spatial Omics Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152658
- ISBN
- 9798384023173
- DDC
- 574
- 저자명
- Coleman, Kyle.
- 서명/저자
- Machine Learning Methods for the Analysis of Multi-Modal Spatial Omics Data
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 119 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Li, Mingyao;Shinohara, Russell T.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약In providing spatial context to molecular expression, spatial omics technologies have profound implications for disease diagnosis, outcome prediction, and the development of novel therapeutics. However, the inherent limitations and complexities of spatial omics data restrict their interpretability and complicate their integration into clinical settings. For instance, sequencing-based spatial transcriptomics data lack single-cell resolution, hindering the precise localization of specific cell types. Additionally, while spatial omics technologies can generate multiple omics and imaging modalities from a single tissue section, effectively integrating these diverse modalities remains a complex task. Moreover, the vast amount of data produced by imaging-based spatial transcriptomics experiments, which can encompass millions of cells, renders traditional spatial omics analysis methods ineffective. This dissertation addresses these challenges by harnessing machine learning to enhance the analysis and interpretation of spatial omics data. First, we developed a cell-type deconvolution method for sequencing-based spatial transcriptomics data that combines gene expression and histology imaging data to accurately estimate spatial distributions of specific cell types. Second, we developed a multi-modal feature extraction and spatial clustering algorithm that can integrate data from any number of omics and imaging modalities. Lastly, we developed a workflow for analyzing data from MERFISH experiments consisting of 16 million cells across eight cortical areas and four developmental stages from the human fetal cortex. Each of these projects aims to bolster the power and interpretability of spatial omics data, thereby accelerating the discovery of novel biological insights and facilitating the incorporation of spatial omics into clinical practice.
- 일반주제명
- Biostatistics
- 일반주제명
- Biomedical engineering
- 일반주제명
- Histology
- 일반주제명
- Computer science
- 일반주제명
- Medical imaging
- 키워드
- Deep learning
- 기타저자
- University of Pennsylvania Statistics and Data Science
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384023173
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aColeman, Kyle.
■24510▼aMachine Learning Methods for the Analysis of Multi-Modal Spatial Omics Data
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a119 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Li, Mingyao;Shinohara, Russell T.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aIn providing spatial context to molecular expression, spatial omics technologies have profound implications for disease diagnosis, outcome prediction, and the development of novel therapeutics. However, the inherent limitations and complexities of spatial omics data restrict their interpretability and complicate their integration into clinical settings. For instance, sequencing-based spatial transcriptomics data lack single-cell resolution, hindering the precise localization of specific cell types. Additionally, while spatial omics technologies can generate multiple omics and imaging modalities from a single tissue section, effectively integrating these diverse modalities remains a complex task. Moreover, the vast amount of data produced by imaging-based spatial transcriptomics experiments, which can encompass millions of cells, renders traditional spatial omics analysis methods ineffective. This dissertation addresses these challenges by harnessing machine learning to enhance the analysis and interpretation of spatial omics data. First, we developed a cell-type deconvolution method for sequencing-based spatial transcriptomics data that combines gene expression and histology imaging data to accurately estimate spatial distributions of specific cell types. Second, we developed a multi-modal feature extraction and spatial clustering algorithm that can integrate data from any number of omics and imaging modalities. Lastly, we developed a workflow for analyzing data from MERFISH experiments consisting of 16 million cells across eight cortical areas and four developmental stages from the human fetal cortex. Each of these projects aims to bolster the power and interpretability of spatial omics data, thereby accelerating the discovery of novel biological insights and facilitating the incorporation of spatial omics into clinical practice.
■590 ▼aSchool code: 0175.
■650 4▼aBiostatistics
■650 4▼aBiomedical engineering
■650 4▼aHistology
■650 4▼aComputer science
■650 4▼aMedical imaging
■653 ▼aDeep learning
■653 ▼aHistology imaging
■653 ▼aMulti-modal spatial omics
■653 ▼aSpatial multi-omics
■653 ▼aSpatial transcriptomics
■690 ▼a0308
■690 ▼a0984
■690 ▼a0541
■690 ▼a0574
■690 ▼a0414
■71020▼aUniversity of Pennsylvania▼bStatistics and Data Science.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163365▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
