CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

Penfold Christopher A., Shifaz Ahmed, Brown Paul E., Nicholson Ann and Wild David L. ()
Additional contact information
Penfold Christopher A.: Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL
Shifaz Ahmed: Faculty of Information Technology, Monash University, VIC, 3800, Australia
Brown Paul E.: Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL
Nicholson Ann: Faculty of Information Technology, Monash University, VIC, 3800, Australia
Wild David L.: Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 3, 307-310

Abstract: Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

Keywords: Bayesian; Gaussian process; gene regulatory networks (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1515/sagmb-2014-0082

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