Gene Regulatory Networks at Single-Cell Resolution: an approach to exploring the impact of genomic regulation on cellular heterogeneity
type: CreativeWork
description: The computational analysis of single-cell RNA sequencing data provides a great opportunity to infer gene regulatory associations in different tissues, cell types and cells. However, there are many challenges still to be overcome. In this thesis, I discuss why this opportunity is fascinating but challenging from a computational point of view, and I build a computational method to demonstrate the plausibility of inferring one gene regulatory network (GRN) for each single cell, also evaluations are conducted from multiple perspectives. In Chapter 2, I investigate data-fitting models in existing computational methods which infer GRNs. To fully take advantage of the single-cell resolution in the input sequencing data, leads to the use of machine learning approaches, such as instance-wise feature selection models. I assess the eligibility at a methodological level, implement an instance-wise feature selection model, and aim to generate GRNs which learn cell-to-cell variations from single-cell sequencing. Based on the implementation above, I build a new method to infer GRN at single-cell resolution or single-cell specific GRN (scGRN). The inferred scGRN can be used to explore GRNs across cell types. Since the true underlying biological mechanism in scGRN cannot be known, my method is benchmarked based on available biological network databases which do not reveal cell-to-cell variation on GRN. Analysis of scRNA-seq data can provide biological insights into cellular heterogeneity because of the single-cell resolution; since scGRNs also contain cell-resolution information, it implies that scGRN can also be analysed about its corresponding cell-related properties. Since scGRN is generated from scRNA-seq data, the inherent cell-related patterns in scGRN shall not deviate substantially from the results in the existing analysis for scRNA-seq. A resulting scGRN suggesting very different cellular information may harm its reliability even before conducting GRN validation, so cellula…
abstract: The computational analysis of single-cell RNA sequencing data provides a great opportunity to infer gene regulatory associations in different tissues, cell types and cells. However, there are many challenges still to be overcome. In this thesis, I discuss why this opportunity is fascinating but challenging from a computational point of view, and I build a computational method to demonstrate the plausibility of inferring one gene regulatory network (GRN) for each single cell, also evaluations are conducted from multiple perspectives. In Chapter 2, I investigate data-fitting models in existing computational methods which infer GRNs. To fully take advantage of the single-cell resolution in the input sequencing data, leads to the use of machine learning approaches, such as instance-wise feature selection models. I assess the eligibility at a methodological level, implement an instance-wise feature selection model, and aim to generate GRNs which learn cell-to-cell variations from single-cell sequencing. Based on the implementation above, I build a new method to infer GRN at single-cell resolution or single-cell specific GRN (scGRN). The inferred scGRN can be used to explore GRNs across cell types. Since the true underlying biological mechanism in scGRN cannot be known, my method is benchmarked based on available biological network databases which do not reveal cell-to-cell variation on GRN. Analysis of scRNA-seq data can provide biological insights into cellular heterogeneity because of the single-cell resolution; since scGRNs also contain cell-resolution information, it implies that scGRN can also be analysed about its corresponding cell-related properties. Since scGRN is generated from scRNA-seq data, the inherent cell-related patterns in scGRN shall not deviate substantially from the results in the existing analysis for scRNA-seq. A resulting scGRN suggesting very different cellular information may harm its reliability even before conducting GRN validation, so cellula…
author: Xu, Zhihan
language: eng
keywords: deep learning method, gene regulatory network, single-cell RNA sequence
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d127ca69-c835-47f6-a293-4c69f6c2a308/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2023-02-10
date modified: 2026-07-22T22:24:30Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.104548