Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements
Cooke, Emma J., Savage, Richard S., Kirk, Paul, Darkins, Robert and Wild, David L. (2011) Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements. BMC Bioinformatics, Vol.12 (No.1). p. 399. ISSN 1471-2105
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Official URL: http://dx.doi.org/10.1186/1471-2105-12-399
Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.
We present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles.
By incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history|
|Divisions:||Faculty of Science > Chemistry
Faculty of Science > Centre for Systems Biology
|Library of Congress Subject Headings (LCSH):||Molecular biology -- Data processing, Time-series analysis, Genes -- Mathematical models|
|Journal or Publication Title:||BMC Bioinformatics|
|Publisher:||Bio Med Central|
|Official Date:||13 October 2011|
|Page Range:||p. 399|
|Access rights to Published version:||Open Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC), Medical Research Council (Great Britain) (MRC), University of Warwick. Molecular Organisation and Assembly in Cells|
|Grant number:||EP/F027400/1 (EPSRC)|
1. Stegle O, Denby KJ, Cooke EJ, Wild DL, Ghahramani Z, Borgwardt KM: A
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