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

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  1. Tian, Tian; Cheng, Ruihua; Wei, Zhi: An empirical Bayes change-point model for transcriptome time-course data (2021)
  2. Granata, Ilaria; Guarracino, Mario R.; Kalyagin, Valery A.; Maddalena, Lucia; Manipur, Ichcha; Pardalos, Panos M.: Model simplification for supervised classification of metabolic networks (2020)
  3. Zheng, Hong: A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus (2019)
  4. Bilgrau, Anders Ellern; Brøndum, Rasmus Froberg; Eriksen, Poul Svante; Dybkær, Karen; Bøgsted, Martin: Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma (2018)
  5. Chen, Ding-Geng (Din); Peace, Karl E.; Zhang, Pinggao: Clinical trial data analysis using R and SAS (2017)
  6. Felici, Giovanni; Tripathi, Kumar Parijat; Evangelista, Daniela; Guarracino, Mario Rosario: A mixed integer programming-based global optimization framework for analyzing gene expression data (2017)
  7. Anders Bilgrau; Poul Eriksen; Jakob Rasmussen; Hans Johnsen; Karen Dybkaer; Martin Boegsted: GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models (2016) not zbMATH
  8. Ibáñez, Kristina; Guijarro, María; Pajares, Gonzalo; Valencia, Alfonso: A computational approach inspired by simulated annealing to study the stability of protein interaction networks in cancer and neurological disorders (2016)
  9. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  10. Mayrink, Vinicius D.; Lucas, Joseph E.: Bayesian factor models for the detection of coherent patterns in gene expression data (2015)
  11. Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)
  12. Gu, Jian-lei; Lu, Yao; Liu, Cong; Lu, Hui: Multiclass classification of sarcomas using pathway based feature selection method (2014)
  13. Hernández-Lobato, Jose Miguel; Hernández-Lobato, Daniel; Suárez, Alberto: Network-based sparse Bayesian classification (2011)
  14. Schmidberger, Markus; Vicedo, Esmeralda; Mansmann, Ulrich: Empirical study for the agreement between statistical methods in quality assessment and control of microarray data (2011)
  15. Hellwig, Birte; Hengstler, Jan G.; Schmidt, Marcus; Gehrmann, Mathias C.; Schormann, Wiebke; Rahnenführer, Jörg: Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes (2010) ioport
  16. Hulsman, Marc; Mentink, Anouk; Van Someren, Eugene P.; Dechering, Koen J.; De Boer, Jan; Reinders, Marcel J. T.: Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization (2010) ioport
  17. McCall, Matthew N.; Bolstad, Benjamin M.; Irizarry, Rafael A.: Frozen robust multiarray analysis (fRMA) (2010)
  18. Williams, Andrew; Thomson, Errol M.: Effects of scanning sensitivity and multiple scan algorithms on microarray data quality (2010) ioport
  19. Dondrup, Michael; Albaum, Stefan P.; Griebel, Thasso; Henckel, Kolja; Jünemann, Sebastian; Kahlke, Tim; Kleindt, Christiane K.; Küster, Helge; Linke, Burkhard; Mertens, Dominik; Mittard-Runte, Virginie; Neuweger, Heiko; Runte, Kai J.; Tauch, Andreas; Tille, Felix; Pühler, Alfred; Goesmann, Alexander: EMMA 2 - A MAGE-compliant system for the collaborative analysis and integration of microarray data (2009) ioport
  20. Du, Rose; Tantisira, Kelan; Carey, Vincent J.; Bhattacharya, Soumyaroop; Metje, Stephanie; Kho, Alvin T.; Klanderman, Barbara J.; Gaedigk, Roger; Lazarus, Ross; Mariani, Thomas J.; Leeder, J. Steven; Weiss, Scott T.: Platform dependence of inference on gene-wise and gene-set involvement in human lung development (2009) ioport

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