Bayesian Methods for Protein Quantification in Mass Spectrometry Proteomics

Phillips, Alexander ORCID: 0000-0002-1637-4803
(2020) Bayesian Methods for Protein Quantification in Mass Spectrometry Proteomics. PhD thesis, University of Liverpool.

[img] Text
200796102_Sep2019.pdf - Unspecified

Download (27MB) | Preview


Current workflows in mass spectrometry proteomics are able to identify thousands of proteins in a single biological sample by breaking them down through enzymatic digestion into smaller molecules, peptides. Quantification of the differences in abundances of proteins between populations is based on measurements of these peptides at multiple charge states, features. Subsequent determination of significant changes in the abundance of proteins between those populations remains a challenge. This is complicated further by the presence of shared peptides arising from multiple proteins. This PhD thesis explores some of the statistical modelling challenges associated with the quantification of proteins in mass spectrometry proteomics. Firstly, an overview of the existing literature is presented, focusing on current methods for quantifying proteins and differential expression analysis. A current Bayesian pipeline for quantifying proteins from feature-level data using Markov chain Monte Carlo sampling is then evaluated. A method by which sampling might be made more efficient by exploiting conjugate distributions is then proposed. Similar conjugate analysis is then applied to the specific problem of differential expression analysis. The analytic nature of the resulting model is exploited to achieve fast inference without the need for computationally expensive numerical integration. This enables a model comparison approach to statistical testing, with the aim of achieving calibrated estimates of false discovery rate. A Bayesian hierarchical model is then proposed for the analysis of shared peptides. Finally, overarching conclusions are drawn and recommendations for future research are discussed.

Item Type: Thesis (PhD)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 17 Aug 2020 10:58
Last Modified: 18 Jan 2023 23:50
DOI: 10.17638/03089147