Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

131Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tan:2017:10.1214/17-AOAS1076,
author = {Tan, LSL and Jasra, A and De, Iorio M and Ebbels, TMD},
doi = {10.1214/17-AOAS1076},
journal = {Annals of Applied Statistics},
pages = {2222--2251},
title = {Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks},
url = {http://dx.doi.org/10.1214/17-AOAS1076},
volume = {11},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - We investigate the effect of cadmium (a toxic environmental pollutant) on the correlation structure of a number of urinary metabolites using Gaussian graphical models (GGMs). The inferred metabolic associations can provide important information on the physiological state of a metabolic system and insights on complex metabolic relationships. Using the fitted GGMs, we construct differential networks, which highlight significant changes in metabolite interactions under different experimental conditions. The analysis of such metabolic association networks can reveal differences in the underlying biological reactions caused by cadmium exposure. We consider Bayesian inference and propose using the multiplicative (or Chung–Lu random graph) model as a prior on the graphical space. In the multiplicative model, each edge is chosen independently with probability equal to the product of the connectivities of the end nodes. This class of prior is parsimonious yet highly flexible; it can be used to encourage sparsity or graphs with a pre-specified degree distribution when such prior knowledge is available. We extend the multiplicative model to multiple GGMs linking the probability of edge inclusion through logistic regression and demonstrate how this leads to joint inference for multiple GGMs. A sequential Monte Carlo (SMC) algorithm is developed for estimating the posterior distribution of the graphs.
AU - Tan,LSL
AU - Jasra,A
AU - De,Iorio M
AU - Ebbels,TMD
DO - 10.1214/17-AOAS1076
EP - 2251
PY - 2017///
SN - 1932-6157
SP - 2222
TI - Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
T2 - Annals of Applied Statistics
UR - http://dx.doi.org/10.1214/17-AOAS1076
UR - http://hdl.handle.net/10044/1/56975
VL - 11
ER -