Imperial College London
Alternate

Professor Alastair Young

Faculty of Natural Sciences, Department of Mathematics

Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8560alastair.young Website

 
 
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Location

 

529Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Young:2015:10.1186/s12859-015-0735-5,
author = {Young, GA and Montana, G and Ruan, D},
doi = {10.1186/s12859-015-0735-5},
journal = {BMC Bioinformatics},
title = {Differential analysis of biological networks},
url = {http://dx.doi.org/10.1186/s12859-015-0735-5},
volume = {16},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundIn cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences.ResultsWe propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation.ConclusionsWe show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.
AU  - Young,GA
AU  - Montana,G
AU  - Ruan,D
DO  - 10.1186/s12859-015-0735-5
PY  - 2015///
SN  - 1471-2105
TI  - Differential analysis of biological networks
T2  - BMC Bioinformatics
UR  - http://dx.doi.org/10.1186/s12859-015-0735-5
UR  - http://hdl.handle.net/10044/1/26790
VL  - 16
ER  -
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