NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

BMC Bioinformatics

Methodology Article

Chiranjib Bhattacharyya¹  Devdatt Dubhashi²  Vinay Jethava²  Goutham N Vemuri³ 

[1] Computer Science and Automation Department, Indian Institute of Science, Bangalore, INDIA;Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg, SWEDEN;Systems Biology Division, Department of Chemical and Biological Engineering, Chalmers University of Technology, SWEDEN;

关键词: Gene Expression Data;    Functional Category;    Interaction Strength;    Protein Interaction Network;    Transition Probability Matrix;   

DOI  :  10.1186/1471-2105-12-327

 received in 2010-11-12, accepted in 2011-08-08,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundTemporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation.ResultsWe present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks.ConclusionsNETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems.The source code for NETGEM is available from https://github.com/vjethava/NETGEM

【 授权许可】

Unknown   
© Jethava et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.