자료유형  

 학위논문

Control Number  

0017163981

International Standard Book Number  

9798384098782

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Persad, Sitara Camini.

Publication, Distribution, etc. (Imprint  

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

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

219 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Pe'er, Itsik.

Dissertation Note  

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

Summary, Etc.  

요약Single-cell sequencing techniques, such as single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), have revolutionized our understanding of cellular diversity and function. Genetic and epigenetic factors influence phenotypic heterogeneity in ways that are just beginning to be understood. In this work, we develop methods for inferring mechanisms of biological heterogeneity in single-cell data, with particular applications to cancer biology. First, we develop a kernel archetype analysis method for overcoming noise and sparsity in single-cell data by aggregating single cells into high-resolution cell states. We show that the proposed approach captures robust and biologically meaningful cell states and enables the inference of epigenetic regulation of phenotypic heterogeneity. In the second part of this thesis, we develop methods for linking genotypic and phenotypic information, first by using aggregated single-cell RNA sequencing and a hidden Markov model to infer copy number variation. We demonstrate that aggregation improves copy number inference over existing approaches. We then integrate DNA sequencing with single-cell RNA sequencing to infer copy number profiles in a rapid autopsy of a patient with metastatic pancreatic cancer. We develop a scalable algorithm for inferring phylogenetic relationships between cells from noisy copy number profiles. We show that our approach more accurately recovers phylogenetic relationships between cells and apply it to understand the relationship between genotype and phenotype in metastatic cancer. Finally, we develop a metric for quantifying the extent to which genotype determines phenotype in lineage tracing data. We show that it more accurately quantifies phenotypic plasticity compared to existing approaches. Altogether, these methods can be used to help uncover the mechanisms underlying phenotypic heterogeneity in biological systems.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Biology.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Genetics.

Index Term-Uncontrolled  

Inferring mechanisms

Index Term-Uncontrolled  

Biological heterogeneity

Index Term-Uncontrolled  

Single-cell data

Index Term-Uncontrolled  

Single-cell RNA sequencing

Index Term-Uncontrolled  

Cancer

Added Entry-Corporate Name  

Columbia University Computer Science

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:656509