Award Date

August 2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Life Sciences

First Committee Member

Mira Han

Second Committee Member

Qian Liu

Third Committee Member

Jingchun Chen

Fourth Committee Member

Vikram Chhatre

Fifth Committee Member

Edwin Oh

Number of Pages

144

Abstract

Alternative Splicing (AS) plays a critical role in transcriptome complexity and cell-type-specific gene regulation, yet its analysis remains methodologically fragmented, especially in the context of noisy and sparse single-cell RNA sequencing (scRNA-seq) data. This dissertation addresses key computational challenges in AS detection by evaluating existing tools, developing integrative frameworks, and proposing new strategies for improving analysis accuracy in both bulk and single-cell contexts. In chapter 1, I present a comprehensive literature review of computational tools designed for detecting and quantifying AS from bulk and scRNA-seq data. This review outlines major methodological paradigms, including exon-based and splice junction-based approaches, and evaluates their underlying statistical models, highlighting limitations in resolution, interpretability, and scalability. Chapter 2 introduces GrASE, a novel splicing graph-based method that unifies exon fragment-based and splice junction-based approaches. This unified framework not only facilitates cross-method benchmarking but also reveals AS events consistent across methods, and method-specific biases using short-read RNA-seq data. Lastly, chapter 3 presents a comprehensive benchmarking framework for differential AS detection in scRNA-seq data. Three count structures: exon counts, splice junction counts, and a newly proposed adjacent exon count, are evaluated in combination with three statistical models: negative binomial, beta-binomial, and mixed binomial. This chapter assesses performance across methods and highlights the trade-offs between statistical power and false discovery. A pseudo-bulking strategy is also explored to mitigate noise and enhance detection sensitivity in single-cell datasets. Collectively, this work advances the methodological landscape for AS analysis by providing a unified modeling framework, benchmarking strategies, and practical guidance for robust detection of splicing variation at single-cell resolution.

Keywords

Alternative Splicing; Benchmarking; Differential Splicing Analysis; Single-cell RNA-seq; Statistical Models; Transcriptome

Disciplines

Bioinformatics | Biology | Biostatistics

File Format

pdf

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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