Univariate and multivariate statistical approaches for the analyses of omics data: sample classification and two-block integration.
File(s)
Author(s)
Garrido Manriquez, Javiera
Type
Thesis
Abstract
The wealth of information generated by high-throughput omics technologies in the
context of large-scale epidemiological studies has made a significant contribution to the identification of factors influencing the onset and progression of common diseases. Advanced computational and statistical modelling techniques are required to manipulate and extract meaningful biological information from these omics data as several layers of complexity are associated with them. Recent research efforts have concentrated in the development of novel statistical and bioinformatic tools; however, studies thoroughly investigating the applicability and suitability of these novel methods in real data have often fallen behind. This thesis focuses in the analyses of proteomics and transcriptomics data from the EnviroGenoMarker project with the purpose of addressing two main research objectives:
i) to critically appraise established and recently developed statistical approaches
in their ability to appropriately accommodate the inherently complex nature
of real-world omics data and ii) to improve the current understanding of a prevalent
condition by identifying biological markers predictive of disease as well as possible
biological mechanisms leading to its onset. The specific disease endpoint of interest
corresponds to B-cell Lymphoma, a common haematological malignancy for which
many challenges related to its aetiology remain unanswered.
The seven chapters comprising this thesis are structured in the following manner:
the first two correspond to introductory chapters where I describe the main omics
technologies and statistical methods employed for their analyses. The third chapter provides a description of the epidemiological project giving rise to the study population and the disease outcome of interest. These are followed by three results chapters that address the research aims described above by applying univariate and multivariate statistical approaches for sample classification and data integration purposes. A summary of findings, concluding general remarks and discussion of open problems offering potential avenues for future research are presented in the final chapter.
context of large-scale epidemiological studies has made a significant contribution to the identification of factors influencing the onset and progression of common diseases. Advanced computational and statistical modelling techniques are required to manipulate and extract meaningful biological information from these omics data as several layers of complexity are associated with them. Recent research efforts have concentrated in the development of novel statistical and bioinformatic tools; however, studies thoroughly investigating the applicability and suitability of these novel methods in real data have often fallen behind. This thesis focuses in the analyses of proteomics and transcriptomics data from the EnviroGenoMarker project with the purpose of addressing two main research objectives:
i) to critically appraise established and recently developed statistical approaches
in their ability to appropriately accommodate the inherently complex nature
of real-world omics data and ii) to improve the current understanding of a prevalent
condition by identifying biological markers predictive of disease as well as possible
biological mechanisms leading to its onset. The specific disease endpoint of interest
corresponds to B-cell Lymphoma, a common haematological malignancy for which
many challenges related to its aetiology remain unanswered.
The seven chapters comprising this thesis are structured in the following manner:
the first two correspond to introductory chapters where I describe the main omics
technologies and statistical methods employed for their analyses. The third chapter provides a description of the epidemiological project giving rise to the study population and the disease outcome of interest. These are followed by three results chapters that address the research aims described above by applying univariate and multivariate statistical approaches for sample classification and data integration purposes. A summary of findings, concluding general remarks and discussion of open problems offering potential avenues for future research are presented in the final chapter.
Version
Open Access
Date Issued
2020-07
Date Awarded
2020-11
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Chadeau, Marc
Vineis, Paolo
Elliott, Paul
Sponsor
Agencia Nacional de Investigación y Desarrollo, Chile (ANID)
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)