자료유형  

 학위논문

Control Number  

0017161653

International Standard Book Number  

9798382807201

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Strzalkowski, Alexander Artur.

Publication, Distribution, etc. (Imprint  

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

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

87 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Raphael, Benjamin J.

Dissertation Note  

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

Summary, Etc.  

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.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Genetics.

Index Term-Uncontrolled  

Single-cell RNA sequencing

Index Term-Uncontrolled  

Trajectory inference

Index Term-Uncontrolled  

Single-cell omics

Index Term-Uncontrolled  

Human health

Added Entry-Corporate Name  

Princeton University Computer Science

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:657814