Item Details

abstract

Single-cell technologies offer a unique opportunity to deepen our understanding of cellular transcription and cellular interaction. However, single-cell RNA-seq data have a hierarchical correlation structure that must be accounted for during differential expression analysis. Computing differential expression analysis with single-cell data as if each cell were independent leads to biased inference and highly inflated type 1 error. In Chapter II of this dissertation, we demonstrate the rationale for utilizing mixed-effects models with a random effect for individual to explicitly model the hierarchical structure of single-cell RNA-seq data. As the field continues to expand, and more single-cell datasets are generated, the application of mixed-effects models with a random effect for individual to compute differential expression analysis will be critical to improving robustness and reproducibility in single-cell research. In Chapter III, we provide a first-of-its-kind R-package designed to approximate the power for tests of differential expression for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. Specifically, this software simulates gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. This R-package is an important addition to single-cell RNA analytic tools as it will help researchers construct experimental designs with appropriate and accurate power. In Chapter IV, we illustrate how diversity measures can be applied to single-cell RNA-seq data to identify communities of cells (e.g., individuals) or communities of transcripts (e.g., cells) with irregular differences in diversity. Overall, this dissertation demonstrates how single-cell experiments, when appropriately and thoughtfully analyzed, have the potential to fundamentally shift our understanding of cellular biology.

subject

Hierarchical correlation

Mixed models

Power calculator

Pseudoreplication

Random effects

Single-cell RNA-sequencing

contributor

Zimmerman, Kip Donald (author)

Langefeld, Carl D (committee chair)

Howard, Timothy D (committee member)

Olivier, Michael (committee member)

Espeland, Mark A (committee member)

Miller, Lance D (committee member)

date

2021-01-13T09:35:25Z (accessioned)

2021 (issued)

degree

Molecular Genetics & Genomics (discipline)

2021-07-12 (liftdate)

embargo

2021-07-12 (terms)

identifier

http://hdl.handle.net/10339/97953 (uri)

language

en (iso)

publisher

Wake Forest University

title

ADDRESSING PSEUDOREPLICATION IN DIFFERENTIAL EXPRESSION ANALYSIS OF SINGLE-CELL RNA-SEQUENCING DATA

type

Dissertation