NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

• Published in: BMC bioinformatics Vol. 12; no. 1; p. 327
• Main Authors: Jethava, Vinay, Bhattacharyya, Chiranjib, Dubhashi, Devdatt, Vemuri, Goutham N
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.08.2011; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation; Life Sciences; Methodology; Methodology Article; Microarrays; Models, Statistical; Networks analysis; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - metabolism; Protein Interaction Network; Transition Probability Matrix; Functional Category; Interaction Strength; Gene Expression Data
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-12-327

Background Temporal 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. Results We 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. Conclusions NETGEM 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