Pathway-driven integration and interpretation of metabolomics and multi-omics data
File(s)
Author(s)
Wieder, Cecilia
Type
Thesis
Abstract
Pathway analysis is a widespread bioinformatics method for functional interpretation of
omics data. Despite its popularity, there is little research, standardisation, or benchmarking
of its applicability tometabolomics data,which is often neglected in favour of sequencingbased
omics. Metabolomics,which involves the high-throughput profiling of small molecules,
is becoming increasingly popular, especially in conjunction with other omics, underscoring
an additional need for interpretable, user-friendly multi-omics integration methods.
In this thesis I have focused on evaluating and developing methods for metabolomics
pathway analysis and pathway-based multi-omics integration. First, I investigated the suitability
of the popular over-representation pathway analysis approach for metabolomics
data, systematically evaluating a series of essential input parameters andmass-spectrometry
data-specific considerations, and suggesting best practice recommendations. Second, I
evaluated for the first time the suitability of single-sample pathway analysis for metabolomics
data, demonstrating the performance of established and newly proposed methods on both
simulated and experimental datasets. Lastly, I leveraged single-sample pathway analysis to
transformmolecular-level omics data to the pathway dimension, alongside state-of-the-art
multivariate predictive models to create the PathIntegrate framework for pathway-based
multi-omics integration. PathIntegrate combines multi-omics data at the pathway level,
able to accurately identify pathway perturbations and predict sample outcomes even in
low signal-to-noise scenarios, and importantly provides a set of readily-interpretable outputs
in the formof multi-omics pathways.
Taken together, this work builds towards standardising metabolomics pathway analysis
workflows, raising awareness of the limitations and data-specific considerations, whilst
proposing innovative methods for tackling biological interpretation and integration.
omics data. Despite its popularity, there is little research, standardisation, or benchmarking
of its applicability tometabolomics data,which is often neglected in favour of sequencingbased
omics. Metabolomics,which involves the high-throughput profiling of small molecules,
is becoming increasingly popular, especially in conjunction with other omics, underscoring
an additional need for interpretable, user-friendly multi-omics integration methods.
In this thesis I have focused on evaluating and developing methods for metabolomics
pathway analysis and pathway-based multi-omics integration. First, I investigated the suitability
of the popular over-representation pathway analysis approach for metabolomics
data, systematically evaluating a series of essential input parameters andmass-spectrometry
data-specific considerations, and suggesting best practice recommendations. Second, I
evaluated for the first time the suitability of single-sample pathway analysis for metabolomics
data, demonstrating the performance of established and newly proposed methods on both
simulated and experimental datasets. Lastly, I leveraged single-sample pathway analysis to
transformmolecular-level omics data to the pathway dimension, alongside state-of-the-art
multivariate predictive models to create the PathIntegrate framework for pathway-based
multi-omics integration. PathIntegrate combines multi-omics data at the pathway level,
able to accurately identify pathway perturbations and predict sample outcomes even in
low signal-to-noise scenarios, and importantly provides a set of readily-interpretable outputs
in the formof multi-omics pathways.
Taken together, this work builds towards standardising metabolomics pathway analysis
workflows, raising awareness of the limitations and data-specific considerations, whilst
proposing innovative methods for tackling biological interpretation and integration.
Version
Open Access
Date Issued
2023-12
Date Awarded
2024-03
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Ebbels, Timothy
Lai, Rachel
Sponsor
Wellcome Trust (London, England)
Grant Number
222837/Z/21/Z
Publisher Department
Department of Metabolism, Digestion and Reproduction
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)