Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks

Asif H.M. Shahzad and Sanguinetti Guido ()
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Asif H.M. Shahzad: School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
Sanguinetti Guido: School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 5, 545-557

Abstract: We present a novel method for simultaneous inference and nonparametric clustering of transcriptional dynamics from gene expression data. The proposed method uses gene expression data to infer time-varying TF profiles and cluster these temporal profiles according to the dynamics they exhibit. We use the latent structure of factorial hidden Markov model to model the transcription factor profiles as Markov chains and cluster these profiles using nonparametric mixture modeling. An efficient Gibbs sampling scheme is proposed for inference of latent variables and grouping of transcriptional dynamics into a priori unknown number of clusters. We test our model on simulated data and analyse its performance on two expression datasets; S. cerevisiae cell cycle data and E. coli oxygen starvation response data. Our results show the applicability of the method for genome wide analysis of expression data.

Keywords: Bayesian Inference; computational Biology; dynamical models (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2012-0010

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