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puma: a Bioconductor package for propagating uncertainty in microarray analysis

Richard D Pearson12*, Xuejun Liu3, Guido Sanguinetti45, Marta Milo6, Neil D Lawrence1 and Magnus Rattray1*

Author Affiliations

1 School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

2 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK

3 College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, PR China

4 Department of Computer Science, University of Sheffield, Regent Court 211 Portobello Street, Sheffield, S1 4DP, UK

5 ChELSI Institute, Department of Chemical and Process Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

6 NIHR Cardiovascular Biomedical Research Unit, Sheffield Teaching Hospitals NHS Trust, Beech Hill Road, Sheffield, S10 2RX, UK

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BMC Bioinformatics 2009, 10:211  doi:10.1186/1471-2105-10-211

Published: 9 July 2009

Abstract

Background

Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied.

Results

puma is a Bioconductor package incorporating a suite of analysis methods for use on Affymetrix GeneChip data. puma extends the differential expression detection methods of previous work from the 2-class case to the multi-factorial case. puma can be used to automatically create design and contrast matrices for typical experimental designs, which can be used both within the package itself but also in other Bioconductor packages. The implementation of differential expression detection methods has been parallelised leading to significant decreases in processing time on a range of computer architectures. puma incorporates the first R implementation of an uncertainty propagation version of principal component analysis, and an implementation of a clustering method based on uncertainty propagation. All of these techniques are brought together in a single, easy-to-use package with clear, task-based documentation.

Conclusion

For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. These methods can be used to improve results from more traditional analyses of microarray data. puma also offers improvements in terms of scope and speed of execution over previously available methods. puma is recommended for anyone working with the Affymetrix GeneChip platform for gene expression analysis and can also be applied more generally.