Kirk, Paul, Griffin, Jim E., Savage, Richard S., Ghahramani, Zoubin and Wild, David L. (2012) Bayesian correlated clustering to integrate multiple datasets. Bioinformatics . ISSN 1367-4803 (In Press)

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Abstract

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets. Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods.

Item Type:	Submitted Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH):	Biology -- Data processing, Data integration (Computer science), Cluster analysis
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Date:	2012
Identification Number:	10.1093/bioinformatics/bts595
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Medical Research Council (Great Britain) (MRC)
Grant number:	EP/I036575/1 (EPSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/50805

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