Brunel University Research Archive (BURA) >
Schools >
School of Information Systems, Computing and Mathematics >
School of Information Systems, Computing and Mathematics Research Papers >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2901

Title: Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets
Authors: Steele, E
Tucker, A
Keywords: Consensus
bayesian network
gene expression
meta analysis
Publication Date: 2008
Publisher: Elsevier
Citation: Journal of Biomedical Informatics. 41(6), pp.914-26, Dec 2008
Abstract: Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation.
URI: http://bura.brunel.ac.uk/handle/2438/2901
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

Files in This Item:

File Description SizeFormat
consensus_steele_preprint.pdf255.59 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan