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Inferring the Biological Time of Single Cells Using Supervised Dimensionality Reduction and Trees
Inferring the Biological Time of Single Cells Using Supervised Dimensionality Reduction and Trees
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151426
- ISBN
- 9798382807201
- DDC
- 574
- 서명/저자
- Inferring the Biological Time of Single Cells Using Supervised Dimensionality Reduction and Trees
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 87 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Raphael, Benjamin J.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Single-cell omics measurements have exploded in growth over the past decade. This explosion has allowed researchers to probe human health and biology with unprecedented resolution. Currently, all these types of measurements are destructive, thus they only provide static snapshots of important dynamic biological processes such as development, cancer progression, and cell cycle. As cells differentiate/progress biologically asynchronously in most tissues, a major computational task often called trajectory inference is to infer the latent biological time also known as pseudotime of every cell. This inverse problem in general is quite challenging and is further complicated by the fact that single-cell omics measurements like scRNA-seq and scATAC-seq are highly sparse and highdimensional. Much of my work has shown that simple linear supervised dimensionality reduction techniques that rely on cell type information can outperform complex non-linear dimensionality reduction techniques when used in conjunction with state-of-the-art trajectory inference methods in a large benchmark. Moreover, we investigate the difficulties of benchmarking trajectory inference methods in the absence of ground truth showcasing that the implicit goal of many methods is not to identify intermediate/transient cell types but rather order cell types. In addition, we introduce a novel supervised linear dimensionality reduction technique called BCA that when applied to simulated and real datasets is better able to uncover intermediate cell types. Lastly, we have been interested in modeling the relationship between cell lineages of inferred phylogenies from single-cell lineage tracing data and scRNA-seq trajectories (the partial ordering of cells induced by pseudotimes). We have found that by using a novel irreversible continuous state model of pseudotime on a rooted tree that we are better able to model unobserved ancestral pseudotimes in simulated and real phylogenies.
- 일반주제명
- Bioinformatics
- 일반주제명
- Computer science
- 일반주제명
- Genetics
- 키워드
- Human health
- 기타저자
- Princeton University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151426
■006m o d
■007cr#unu||||||||
■020 ▼a9798382807201
■035 ▼a(MiAaPQ)AAI31294770
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aStrzalkowski, Alexander Artur.▼0(orcid)0000-0002-4592-3533
■24510▼aInferring the Biological Time of Single Cells Using Supervised Dimensionality Reduction and Trees
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a87 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Raphael, Benjamin J.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aSingle-cell omics measurements have exploded in growth over the past decade. This explosion has allowed researchers to probe human health and biology with unprecedented resolution. Currently, all these types of measurements are destructive, thus they only provide static snapshots of important dynamic biological processes such as development, cancer progression, and cell cycle. As cells differentiate/progress biologically asynchronously in most tissues, a major computational task often called trajectory inference is to infer the latent biological time also known as pseudotime of every cell. This inverse problem in general is quite challenging and is further complicated by the fact that single-cell omics measurements like scRNA-seq and scATAC-seq are highly sparse and highdimensional. Much of my work has shown that simple linear supervised dimensionality reduction techniques that rely on cell type information can outperform complex non-linear dimensionality reduction techniques when used in conjunction with state-of-the-art trajectory inference methods in a large benchmark. Moreover, we investigate the difficulties of benchmarking trajectory inference methods in the absence of ground truth showcasing that the implicit goal of many methods is not to identify intermediate/transient cell types but rather order cell types. In addition, we introduce a novel supervised linear dimensionality reduction technique called BCA that when applied to simulated and real datasets is better able to uncover intermediate cell types. Lastly, we have been interested in modeling the relationship between cell lineages of inferred phylogenies from single-cell lineage tracing data and scRNA-seq trajectories (the partial ordering of cells induced by pseudotimes). We have found that by using a novel irreversible continuous state model of pseudotime on a rooted tree that we are better able to model unobserved ancestral pseudotimes in simulated and real phylogenies.
■590 ▼aSchool code: 0181.
■650 4▼aBioinformatics
■650 4▼aComputer science
■650 4▼aGenetics
■653 ▼aSingle-cell RNA sequencing
■653 ▼aTrajectory inference
■653 ▼aSingle-cell omics
■653 ▼aHuman health
■690 ▼a0715
■690 ▼a0984
■690 ▼a0369
■71020▼aPrinceton University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161653▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
