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ISSN: 2157-7420

Journal of Health & Medical Informatics
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  • Research Article   
  • J Health Med Informat 2011, Vol 4(2)
  • DOI: 10.4172/2157-7420.S2-001

Divergence Weighted Independence Graphs for the Exploratory Analysis of Biological Expression Data

Yang Xiang1, Marja Talikka1, Vincenzo Belcastro1, Peter Sperisen1, Manuel C. Peitsch1, Julia Hoeng1* and Joe Whittaker2*
1Department of Biological System Research, Philip Morris International R&D, , Neuchatel, Switzerland
2Department of Mathematics and Statistics, Lancaster University, UK
*Corresponding Author (s) : Julia Hoeng, Department of Biological System Research, Philip Morris International R&D, Neuchatel, Switzerland, Tel: +41 (58) 242 2214, Fax: +41 (58) 242 2811, Email: [email protected]
Joe Whittaker, Department of Mathematics and Statistics, Lancaster University, UK, Email: [email protected]

Received Date: Nov 19, 2011 / Accepted Date: Dec 12, 2011 / Published Date: Dec 16, 2011

Abstract

Motivation: Understanding biological processes requires tools for the exploratory analysis of multivariate data generated from in vitro and in vivo experiments. Part of such analyses is to visualise the interrelationships between observed variables. Results: We build on recent work using partial correlation, graphical Gaussian models, and stability selection to add divergence weighted independence graphs (DWIGs) to this toolbox. We measure all quantities in information units (bits and millibits), to give a common quantification of the strength of associations between variables and of the information explained by a fitted graphical model. The marginal mutual information (MI) and conditional MI between variables directly account for components of the information explained. The conditional MIs are displayed as edge weights in the independence graph of the variables, making the complete graph informative as to the unique association between those variables. The summary table of the information decomposition ‘total = explained + residual’ provides a simple comparison of graphical models suggested by different search routines, including stabilised versions. We demonstrate the relevance of the conditional MI statistics to the graphical model of the data by analysing simulated data from the insulin pathway with a known ground truth. Here the method of thresholding these statistics to suggest a network performs at least as well as several other network searching algorithms. In searching a biological data set for novel insight, we contrast the DWIGs from the fitted maximum weight spanning tree and from the fitted model of a stabilised ARACNE network. DWIG is a powerful tool for the display of properties of the fitted model or of the empirical data directly.

Keywords: Bronchoalveolar lavage fluid (BALF); Divergence weighted independence graphs (DWIG); Conditional mutual information (CMI); Graphical gaussian model (GGM)

Citation: Xiang Y, Talikka M, Belcastro V, Sperisen P, Peitsch MC, et al. (2011) Divergence Weighted Independence Graphs for the Exploratory Analysis of Biological Expression Data. J Health Med Informat S2:001. Doi: 10.4172/2157-7420.S2-001

Copyright: © 2011 Xiang Y, 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.

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