Modelling populations of complex networks

Many real-life systems can be modelled as complex networks, where the agents of the system are represented as nodes and the ties between those agents are represented as edges. Recent advances in data collection technologies give rise to various populations of networks, which capture different aspects of the data. In this thesis we make an essential progress in the modelling and analysis of three different populations of complex networks. First, in real-life systems involving measurements obtained from a population of participants, the system may be described by a population of networks where each participant is himself described by a whole network. We formulate a relevant genomics problem by constructing such a population of complex networks, and address a series of biological hypothesis which involve the clustering and classification of this population of networks. Second, real-life situations are modelled as a multiplex network where each layer of the multiplex captures different type of relationships across the same set of nodes. The nature of the multiplex network raises the question of whether the same connectivity patterns fit all layers. We use a community detection procedure to address this problem, where random walks on the multiplex are used to detect shared and non-shared community structures across the layers of the multiplex. Third, the interactions between the entities of a system that evolve in time are formalized as a temporal network. When the number of entities in the network is very large, different levels of detail and how they change in time are interesting. We use a multi-scale community detection procedure to solve the problems by applying spectral graph wavelets on the temporal network to detect changes in the community structure that occur in more than one scale.Open Acces

Exploring brain transcriptomic patterns:A topological analysis using spatial expression networks

Characterizing the transcriptome architecture of the human brain is fundamental in gaining an understanding of brain function and disease. A number of recent studies have investigated patterns of brain gene expression obtained from an extensive anatomical coverage across the entire human brain using experimental data generated by the Allen Human Brain Atlas (AHBA) project. In this paper, we propose a new representation of a gene's transcription activity that explicitly captures the pattern of spatial co-expression across different anatomical brain regions. For each gene, we define a Spatial Expression Network (SEN), a network quantifying co-expression patterns amongst several anatomical locations. Network similarity measures are then employed to quantify the topological resemblance between pairs of SENs and identify naturally occurring clusters. Using network-theoretical measures, three large clusters have been detected featuring distinct topological properties. We then evaluate whether topological diversity of the SENs reflects significant differences in biological function through a gene ontology analysis. We report on evidence suggesting that one of the three SEN clusters consists of genes specifically involved in the nervous system, including genes related to brain disorders, while the remaining two clusters are representative of immunity, transcription and translation. These findings are consistent with previous studies showing that brain gene clusters are generally associated with one of these three major biological processes.Comment: 8 pages, 4 figures, 2 tables, conferenc

Detection of stable community structures within gut microbiota co-occurrence networks from different human populations populations

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses

Blood protein assessment of leading incident diseases and mortality in the UK Biobank.

Acknowledgements: This research was funded, in whole or in part, by the Wellcome Trust (108890/Z/15/Z). For the purpose of open access, the authors have applied for a CC BY public copyright license to any author-accepted manuscript version arising from this submission. R.E.M. is supported by Alzheimer’s Society major project grant AS-PG-19b-010. R.F.H. is supported by a fellowship from the Medical Research Council Integrative Epidemiology Unit. D.A.G. is supported by the Wellcome Trust Translational Neuroscience program (108890/Z/15/Z). These funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the participants, contributors and researchers of the UK Biobank for making data available for this study. We thank the research and development teams at the 13 participating UKB-PPP companies (Alnylam Pharmaceuticals, Amgen, AstraZeneca, Biogen, Calico, Bristol-Myers Squibb, Genentech, GlaxoSmithKlein (GSK), Janssen Pharmaceuticals, Novo Nordisk, Pfizer, Regeneron and Takeda) for funding the study. We thank the legal and business development teams at each company for overseeing the contracting of this complex, precompetitive collaboration. Our special thanks are extended in particular to E. Olson of Amgen, A. Walsh of GSK and F. Middleton of AstraZeneca. The Biogen team is thankful to H. McLaughlin in relation to her project management support. Finally, we thank the team at Olink Proteomics (P. Pettingell, K. Diamanti, C. Lawley, L. Jung, S. Ghalib, I. Grundberg and J. Heimer) for their logistic support, with special thanks to E. Mills for leading internal activities at Olink. All 13 companies listed as part of the UKB-PPP were involved in the generation of the proteomic data used in the present study. However, only Biogen-affiliated authors were involved in the study design, analysis and decision to publish the current study. Biogen funded the collaboration between Optima Partners and the University of Edinburgh, which provided consultancy fees to D.A.G., R.F.H. and R.E.M. for their involvement in leading the present study.Funder: Supported by the Wellcome Trust 4-year PhD scheme in Translational Neuroscience through Edinburgh University 108890/Z/15/ZFunder: Supported by an MRC IEU fellowship.Funder: Employee of BiogenFunder: Alzheimers society major project grant AS-PG-19b-010The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification