Tools and techniques for single-cell RNA sequencing data
RNA sequencing of individual cells allows us to take a snapshot of the dynamic processes within a cell and explore differences between cell types. As this technology has developed over the last few years it has been rapidly adopted by researchers in areas such as developmental biology, and many single-cell RNA sequencing datasets are now available. Coinciding with the development of protocols for producing single-cell RNA sequencing data there has been a simultaneous burst in the development of computational analysis methods. My thesis explores the computational tools and techniques for analysing single-cell RNA sequencing data. I present a database that charts the release of analysis software, where it has been published and what it can be used for, as well as a website that makes this information publicly available. I also present two of my own tools and techniques including Splatter, a software package for easily simulating single-cell datasets from multiple models, and clustering trees, a visualisation approach for inspecting clustering at multiple resolutions. In the final part of my thesis I perform analysis of a dataset from kidney organoids to demonstrate and compare some current analysis methods. Taken together, my thesis covers many aspects of the tools and techniques for single-cell RNA sequencing by describing the approaches that are available, presenting software that can help in developing and evaluating methods, introducing an approach for aiding one of the most common analysis tasks, and showing how tools can be used to extract meaning from a real dataset.
Citation
@phdthesis{zappia2019,
author = {Zappia, Luke},
editor = {Oshlack, Alicia and H Little, Melissa},
title = {Tools and Techniques for Single-Cell {RNA} Sequencing Data},
date = {2019-08-12},
url = {https://lazappi.id.au/publications/2019-zappia-thesis},
doi = {10.1136/gutjnl-2016-311447},
langid = {en},
abstract = {RNA sequencing of individual cells allows us to take a
snapshot of the dynamic processes within a cell and explore
differences between cell types. As this technology has developed
over the last few years it has been rapidly adopted by researchers
in areas such as developmental biology, and many single-cell RNA
sequencing datasets are now available. Coinciding with the
development of protocols for producing single-cell RNA sequencing
data there has been a simultaneous burst in the development of
computational analysis methods. My thesis explores the computational
tools and techniques for analysing single-cell RNA sequencing data.
I present a database that charts the release of analysis software,
where it has been published and what it can be used for, as well as
a website that makes this information publicly available. I also
present two of my own tools and techniques including Splatter, a
software package for easily simulating single-cell datasets from
multiple models, and clustering trees, a visualisation approach for
inspecting clustering at multiple resolutions. In the final part of
my thesis I perform analysis of a dataset from kidney organoids to
demonstrate and compare some current analysis methods. Taken
together, my thesis covers many aspects of the tools and techniques
for single-cell RNA sequencing by describing the approaches that are
available, presenting software that can help in developing and
evaluating methods, introducing an approach for aiding one of the
most common analysis tasks, and showing how tools can be used to
extract meaning from a real dataset.}
}