puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis

[edit]

Richard D. Pearson
Xuejun Liu, Nanjing University of Aeronautics and Astronautics
Guido Sanguinetti, University of Edinburgh
Marta Milo, University of Sheffield
Neil D. Lawrence, University of Sheffield
Magnus Rattray, University of Manchester

BMC Bioinformatics 10

Related Material

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.


@Article{pearson-puma09,
  title = 	 {puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis},
  journal =  	 {BMC Bioinformatics},
  author = 	 {Richard D. Pearson and Xuejun Liu and Guido Sanguinetti and Marta Milo and Neil D. Lawrence and Magnus Rattray},
  year = 	 {2009},
  volume = 	 {10},
  number =       {211},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2009-01-01-pearson-puma09.md},
  url =  	 {http://inverseprobability.com/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.},
  key = 	 {Pearson-puma09},
  doi = 	 {10.1186/1471-2105-10-211},
  group = 	 {puma}
 

}
%T puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
%A Richard D. Pearson and Xuejun Liu and Guido Sanguinetti and Marta Milo and Neil D. Lawrence and Magnus Rattray
%B 
%C BMC Bioinformatics
%D 
%F pearson-puma09
%J BMC Bioinformatics	
%P --
%R 10.1186/1471-2105-10-211
%U http://inverseprobability.com/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.
TY  - CPAPER
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
PY  - 2009/01/01
DA  - 2009/01/01	
ID  - pearson-puma09	
SP  - 
EP  - 
DO  - 10.1186/1471-2105-10-211
UR  - http://inverseprobability.com/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  -

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):-