puma: a Bioconductor package for propagating uncertainty in microarray analysis

BackgroundMost 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.Resultspuma 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.ConclusionFor 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.

[1]  Neil D. Lawrence,et al.  A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips , 2005, Bioinform..

[2]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[3]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[4]  Neil D. Lawrence,et al.  Accounting for probe-level noise in principal component analysis of microarray data , 2005, Bioinform..

[5]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[6]  Richard D. Pearson,et al.  A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods , 2008, BMC Bioinformatics.

[7]  Magnus Rattray,et al.  Including probe-level uncertainty in model-based gene expression clustering , 2006, BMC Bioinformatics.

[8]  Gang Qu,et al.  AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets , 2007, Bioinform..

[9]  Rafael A. Irizarry,et al.  Comparison of Affymetrix GeneChip expression measures , 2006, Bioinform..

[10]  Peter Spellucci,et al.  An SQP method for general nonlinear programs using only equality constrained subproblems , 1998, Math. Program..

[11]  Neil D. Lawrence,et al.  Probe-level measurement error improves accuracy in detecting differential gene expression , 2006, Bioinform..

[12]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[13]  Terence P. Speed,et al.  A benchmark for Affymetrix GeneChip expression measures , 2004, Bioinform..

[14]  Neil D. Lawrence,et al.  puma User Guide , 2007 .

[15]  Simon Tavaré,et al.  beadarray: R classes and methods for Illumina bead-based data , 2007, Bioinform..