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Title: Bringing models to the domain : deploying Gaussian processes in the biological sciences
Author: Zwiessele, Max
ISNI:       0000 0004 6424 0193
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
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Recent developments in single cell sequencing allow us to elucidate processes of individual cells in unprecedented detail. This detail provides new insights into the progress of cells during cell type differentiation. Cell type heterogeneity shows the complexity of cells working together to produce organ function on a macro level. The understanding of single cell transcriptomics promises to lead to the ultimate goal of understanding the function of individual cells and their contribution to higher level function in their environment. Characterizing the transcriptome of single cells requires us to understand and be able to model the latent processes of cell functions that explain biological variance and richness of gene expression measurements. In this thesis, we describe ways of jointly modelling biological function and unwanted technical and biological confounding variation using Gaussian process latent variable models. In addition to mathematical modelling of latent processes, we provide insights into the understanding of research code and the significance of computer science in development of techniques for single cell experiments. We will describe the process of understanding complex machine learning algorithms and translating them into usable software. We then proceed to applying these algorithms. We show how proper research software design underlying the implementation can lead to a large user base in other areas of expertise, such as single cell gene expression. To show the worth of properly designed software underlying a research project, we show other software packages built upon the software developed during this thesis and how they can be applied to single cell gene expression experiments. Understanding the underlying function of cells seems within reach through these new techniques that allow us to unravel the transcriptome of single cells. We describe probabilistic techniques of identifying the latent functions of cells, while focusing on the software and ease-of-use aspects of supplying proper research code to be applied by other researchers.
Supervisor: Lawrence, Neil Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available