puma: a Bioconductor package for propagating uncertainty in microarray analysis

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.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.The analysis of microarray experiments typically involves a number of stages. The first stage for analysis of Affymetrix GeneChip arrays is usually the application of a summarisation method such as MAS5.0 or RMA in order to obtain an expression level for each probeset on each array. Subsequent analyses then use these expression levels, for example to determine differentially expressed (DE) genes, or to find clusters of genes and/or conditions. Although there are a number of summarisation methods which can give accurate point estimates of expression levels, few can also provide any information about uncertainty