puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis

Richard D. Pearson; Xuejun Liu; Guido Sanguinetti; Marta Milo; Neil D. Lawrence; Magnus Rattray

doi:10.1186/1471-2105-10-211

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puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis

Richard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray

BMC Bioinformatics, 10(211), 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.

Conclusions 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.

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BibTeX


@Article{Pearson-puma09,
  title = 	 {puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis},
  author = 	 {Pearson, Richard D. and Liu, Xuejun and Sanguinetti, Guido and Milo, Marta and Lawrence, Neil D. and Rattray, Magnus},
  journal =      {BMC Bioinformatics},
  year = 	 {2009},
  volume = 	 {10},
  number =       {211},
  doi = 	 {10.1186/1471-2105-10-211},
  pdf = 	 {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-211.pdf},
  url = 	 {/publications/pearson-puma09.html},
  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.

Conclusions
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.}
}

Endnote

%0 Journal Article
%T puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
%A Richard D. Pearson
%A Xuejun Liu
%A Guido Sanguinetti
%A Marta Milo
%A Neil D. Lawrence
%A Magnus Rattray
%J BMC Bioinformatics
%D 2009	
%F Pearson-puma09
%R 10.1186/1471-2105-10-211
%U /publications/pearson-puma09.html
%V 10
%N 211
%X 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.

Conclusions
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.

RIS


TY  - JOUR
TI  - puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
AU  - Richard D. Pearson
AU  - Xuejun Liu
AU  - Guido Sanguinetti
AU  - Marta Milo
AU  - Neil D. Lawrence
AU  - Magnus Rattray
DA  - 2009/07/09	
ID  - Pearson-puma09
VL  - 10
IS  - 211
DO  - 10.1186/1471-2105-10-211
L1  - https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-211.pdf
UR  - /publications/pearson-puma09.html
AB  - 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.

Conclusions
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.
ER  -

APA


Pearson, R.D., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N.D. & Rattray, M.. (2009). puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis. BMC Bioinformatics 10(211) doi:10.1186/1471-2105-10-211 Available from /publications/pearson-puma09.html.