자료유형  

 학위논문

Control Number  

0017160445

International Standard Book Number  

9798383565797

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Qu, Rihao.

Publication, Distribution, etc. (Imprint  

[S.l.] : Yale University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

92 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Yuval, Kluger;Flavell, Richard A.

Dissertation Note  

Thesis (Ph.D.)--Yale University, 2024.

Summary, Etc.  

요약Single-cell sequencing techniques have revolutionized our understanding of cellular diversity within tissues and organs. Typically, single-cell data is preprocessed into a feature-by-cell count matrix, where feature values represent gene expression, chromatin accessibility, protein levels, etc. In the realm of computational analysis, cells are conceptualized as points in a high-dimensional feature space. Under distinct conditions or due to experimental perturbations, specific cellular states may exhibit differential abundance. This can be detected by comparing cell density distributions within the feature space. In Chapter 1, we present a novel computational framework by employing a random-walk-based local two-sample test. This approach enables multiscale, cluster-free, differential cell abundance analysis with rigorous statistical guarantees. Through applications to real-world datasets, our approach captures meaningful variations in cell abundance between different biological conditions and provides new biological insights.Beyond comparing cellular profiles across various datasets, each dataset itself often encapsulates a variety of cellular states that underlie dynamic processes such as cell cycle and tissue/organ differentiation. Current cell trajectory inference approaches use single-cell whole-transcriptome data to organize cells into lineages and assign pseudotime to them. However, many complex biological processes are orchestrated by multiple gene programs, some of which co-occur in an intertwined manner (e.g., cell differentiation coupled with cell cycle). If different sets of genes drive multiple processes in the same group of cells, these cells tend to organize into a manifold with an intrinsic dimension greater than 1. Consequently, determining a unidimensional lineage for these cells and constructing a meaningful cell pseudotime becomes impractical.To overcome this limitation, we have developed a new approach, GeneTrajectory, that constructs trajectories of genes rather than trajectories of cells. GeneTrajectory automatically dissects out gene programs from the whole transcriptome, eliminating the need for initial cell trajectory construction or specification of the initial and terminal cell states for each process. This makes it broadly applicable, even to a cell cloud with a non-curvilinear geometric structure. Using this method, genes that sequentially contribute to a given biological process can be extracted and then organized into a gene trajectory. The ordering of genes along each gene trajectory implies the successive order of gene activity during each underlying biological process. By deconvolving co-occurring processes, each process (e.g., lineage differentiation) can be purely represented, excluding irrelevant biological effects from the other processes. We demonstrate the utility and advantages of GeneTrajectory through applications to two real-world biological datasets in Chapter 2.Throughout my Ph.D., we harnessed cutting-edge single-cell analysis methods to explore diverse biological systems. In Chapter 3, we summarize our endeavors to resolve a fast transition process during the early embryonic stage of mouse hair follicle genesis. We integrated comparative analysis tools to dissect out transcriptome differences between multiple pairs of wildtype and mutant samples, unraveling the molecular mechanisms that orchestrate cellular fate during this developmental process.

Subject Added Entry-Topical Term  

Biology.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Cellular biology.

Subject Added Entry-Topical Term  

Developmental biology.

Subject Added Entry-Topical Term  

Genetics.

Index Term-Uncontrolled  

Comparative analysis

Index Term-Uncontrolled  

Differential abundance analysis

Index Term-Uncontrolled  

Gene trajectory inference

Index Term-Uncontrolled  

Single-cell omics

Index Term-Uncontrolled  

Gene dynamics

Index Term-Uncontrolled  

Single-cell sequencing

Added Entry-Corporate Name  

Yale University Computational Biology and Bioinformatics

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:657369