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Propagating Uncertainty in Microarray Analysis

Bioconductor version: 2.13

Most analyses of Affymetrix GeneChip data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting and data manipulation functions.

Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang

Maintainer: Richard Pearson <richard.pearson at>

Citation (from within R, enter citation("puma")):


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PDF puma-014.pdf
PDF puma-015.pdf
PDF puma-016.pdf
PDF puma-022.pdf
PDF puma-023.pdf
PDF puma-024.pdf
PDF puma.pdf
PDF   Reference Manual


biocViews Bioinformatics, Clustering, DifferentialExpression, Microarray, OneChannel, Preprocessing, Software
Version 3.4.0
In Bioconductor since BioC 2.0 (R-2.5)
License LGPL
Depends R (>= 2.10), Biobase(>= 2.5.5), affy(>= 1.23.4), graphics, grDevices, methods, stats, utils, mclust
Imports Biobase(>= 2.5.5), affy(>= 1.23.4), affyio
Suggests pumadata, affydata, snow, limma, annotate, ROCR
System Requirements
Depends On Me pumadata
Imports Me tigre
Suggests Me tigre

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Package Source puma_3.4.0.tar.gz
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