al Basatena, NKS;
al Basatena, NKS;
de Iorio, M;
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A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes.
, Article e24702. 10.1371/journal.pone.0024702.
Background: Variations in the pattern of molecular associations are observed during disease development. The comprehensive analysis of molecular association patterns and their changes in relation to different physiological conditions can yield insight into the biological basis of disease-specific phenotype variation.Methodology: Here, we introduce a formal statistical method for the differential analysis of molecular associations via network representation. We illustrate our approach with extensive data on lipoprotein subclasses measured by NMR spectroscopy in 4,406 individuals with normal fasting glucose, and 531 subjects with impaired fasting glucose (prediabetes). We estimate the pair-wise association between measures using shrinkage estimates of partial correlations and build the differential network based on this measure of association. We explore the topological properties of the inferred network to gain insight into important metabolic differences between individuals with normal fasting glucose and prediabetes.Conclusions/Significance: Differential networks provide new insights characterizing differences in biological states. Based on conventional statistical methods, few differences in concentration levels of lipoprotein subclasses were found between individuals with normal fasting glucose and individuals with prediabetes. By performing the differential analysis of networks, several characteristic changes in lipoprotein metabolism known to be related to diabetic dyslipidemias were identified. The results demonstrate the applicability of the new approach to identify key molecular changes inaccessible to standard approaches.
|Title:||A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes|
|Open access status:||An open access version is available from UCL Discovery|
|Additional information:||© 2011 Valcárcel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This work was supported by: Biotechnology and Biological Sciences Research Council (Grant Ref.BB/E20372/1); Academy of Finland (grant number 137870); Responding to Public Health Challenges Research Programme of the Academy of Finland (grant number 129429); The Finnish Cardiovascular Research Foundation; The Jenny and Antti Wihuri Foundation; Academy of Finland and the Instrumentarium Science Foundation; The Academy of Finland (project grants 104781, 120315, 1114194, 129269 (SALVE), 139900S); University Hospital Oulu, Biocenter, University of Oulu, Finland; The Medical Research Council (PrevMetSyn), UK and the ENGAGE project and grant agreement HEALTH-F4-2007-201413. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.|
|Keywords:||NUCLEAR-MAGNETIC-RESONANCE, METABOLIC NETWORKS, ORGANIZATION, POPULATION, PARTICLES, COHORT, VLDL|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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