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CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
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Penfold, Christopher A., Shifaz, Ahmed, Brown, Paul E., Nicholson, Ann and Wild, David L. (2015) CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data. Statistical Applications in Genetics and Molecular Biology, Volume 14 (Number 3). pp. 307-310. doi:10.1515/sagmb-2014-0082
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Official URL: http://dx.doi.org/10.1515/sagmb-2014-0082
Abstract
How an organism responds to the environmental challenges it faces is heavily influenced by its gene regulatory networks (GRNs). Whilst most methods for inferring GRNs from time series mRNA expression data are only able to cope with single time series (or single perturbations with biological replicates), it is becoming increasingly common for several time series to be generated under different experimental conditions. The CSI algorithm (Klemm, 2008) represents one approach to inferring
GRNs from multiple time series data, which has previously been shown to perform well on a variety of datasets (Penfold and Wild, 2011). Another challenge in network inference is the identification of condition specific GRNs i.e., identifying how a GRN is rewired under different conditions or different individuals. The Hierarchical Causal Structure Identification (HCSI) algorithm (Penfold et al., 2012) is one approach that allows inference of condition specific networks (Hickman et al.,
2013), that has been shown to be more accurate at reconstructing known networks than inference on the individual datasets alone. Here we describe a MATLAB implementation of CSI/HCSI that includes fast approximate solutions to CSI as well as Markov Chain Monte Carlo implementations of both CSI and HCSI, together with a user-friendly GUI, with the intention of making the analysis of networks from multiple perturbed time series datasets more accessible to the wider community.1 The GUI itself guides the user through each stage of the analysis, from loading in the data, to parameter selection and visualisation of networks, and can be launched by typing >> csi into the MATLAB command line. For each step of the analysis, links to documentation and tutorials are available within the GUI, which includes documentation on visualisation and interacting with output files
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QH Natural history > QH426 Genetics | ||||||
| Divisions: | Faculty of Science > Centre for Systems Biology | ||||||
| Library of Congress Subject Headings (LCSH): | Gene regulatory networks | ||||||
| Journal or Publication Title: | Statistical Applications in Genetics and Molecular Biology | ||||||
| Publisher: | Walter de Gruyter | ||||||
| ISSN: | 1544-6115 | ||||||
| Official Date: | June 2015 | ||||||
| Dates: |
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| Date of first compliant deposit: | 29 December 2015 | ||||||
| Volume: | Volume 14 | ||||||
| Number: | Number 3 | ||||||
| Number of Pages: | 4 | ||||||
| Page Range: | pp. 307-310 | ||||||
| DOI: | 10.1515/sagmb-2014-0082 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Monash University | ||||||
| Grant number: | EP/I036575/1 (EPSRC), EP/J020281/1 (EPSRC), BB/F005806/1 (BBSRC) |
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