An R Package for Analyses of Affymetrix Oligonucleotide Arrays. We describe an extensible, interactive environment for data analysis and exploration of Affymetrix oligonucleotide array probe-level data. The software utilities provided with the Affymetrix analysis suite summarize the probe set intensities and makes available only one expression measure for each gene. We have developed this package because much can be learned from studying the individual probe intensities or, as we call them, the probe-level data. We provide some examples demonstrating that having access to and methods for probelevel data results in improvements to quality control assessments, normalization, and expression measures. The software is implemented as an add-on package, conveniently named affy, to the freely available and widely used statistical language/software R (Ihaka and Gentleman, 1996). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions and powerful graphics capabilities that are provided with R. Our package is distributed as open source code for Linux, Unix, and Microsoft Windows. It is is released under the GNU General Public License. It is part of the Bioconductor project and can be obtained from

References in zbMATH (referenced in 17 articles )

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  1. Felici, Giovanni; Tripathi, Kumar Parijat; Evangelista, Daniela; Guarracino, Mario Rosario: A mixed integer programming-based global optimization framework for analyzing gene expression data (2017)
  2. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016)
  3. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  4. Mayrink, Vinicius D.; Lucas, Joseph E.: Bayesian factor models for the detection of coherent patterns in gene expression data (2015)
  5. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  6. Gu, Jian-lei; Lu, Yao; Liu, Cong; Lu, Hui: Multiclass classification of sarcomas using pathway based feature selection method (2014)
  7. Hernández-Lobato, Jose Miguel; Hernández-Lobato, Daniel; Suárez, Alberto: Network-based sparse Bayesian classification (2011)
  8. Schmidberger, Markus; Vicedo, Esmeralda; Mansmann, Ulrich: Empirical study for the agreement between statistical methods in quality assessment and control of microarray data (2011)
  9. Gabriel C. G. de Abreu, Rodrigo Labouriau, David Edwards: High-dimensional Graphical Model Search with gRapHD R Package (2009) arXiv
  10. Hossain, Ahmed; Beyene, Joseph; Willan, Andrew R.; Hu, Pingzhao: A flexible approximate likelihood ratio test for detecting differential expression in microarray data (2009)
  11. Parrish, Rudolph S.; Spencer, Horace J. III; Xu, Ping: Distribution modeling and simulation of gene expression data (2009)
  12. Guillot, Gilles; Olsson, Maja; Benson, Mikael; Rudemo, Mats: Discrimination and scoring using small sets of genes for two-sample microarray data (2007)
  13. Hu, Jianhua; He, Xuming: Enhanced quantile normalization of microarray data to reduce loss of information in gene expression profiles (2007)
  14. Opgen-Rhein, Rainer; Strimmer, Korbinian: Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach (2007)
  15. Klebanov, L.; Gordon, A.; Xiao, Y.; Land, H.; Yakovlev, A.: A permutation test motivated by microarray data analysis (2006)
  16. Gentleman, Robert (ed.); Carey, Vincent J. (ed.); Huber, Wolfgang (ed.); Irizarry, Rafael A. (ed.); Dudoit, Sandrine (ed.): Bioinformatics and computational biology solutions using R and Bioconductor. (2005)
  17. Bryan, Jenny: Problems in gene clustering based on gene expression data (2004)