Affycomp III

Affycomp III. A Benchmark for Affymetrix GeneChip Expression Measures. Motivation: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. Results: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data. Availability: In conjunction with the release of a graphics toolbox as part of the Bioconductor project (http://www.bioconductor.org), a webtool is available at http://affycomp.biostat.jhsph.edu. Supplemental material is available at http://www.biostat.jhsph.edu/ ririzarr/papers/suppaffycomp.pdf


References in zbMATH (referenced in 10 articles )

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  1. Pan, Jia-Chiun; Huang, Yufen; Hwang, J. T. Gene: Estimation of selected parameters (2017)
  2. Khamiakova, Tatsiana; Shkedy, Ziv; Amaratunga, Dhammika; Talloen, Willem; Göhlmann, Hinrich; Bijnens, Luc; Kasim, Adetayo: Quality control of platinum spike dataset by probe-level mixed models (2014)
  3. Purutçuoǧlu, Vilda: Robust gene expression index (2012)
  4. Gormley, Michael; Akella, Viswanadha U.; Quong, Judy N.; Quong, Andrew A.: An integrated framework to model cellular phenotype as a component of biochemical networks (2011) ioport
  5. Pearson, Richard D.; Liu, Xuejun; Sanguinetti, Guido; Milo, Marta; Lawrence, Neil D.; Rattray, Magnus: Puma: a bioconductor package for propagating uncertainty in microarray analysis (2009) ioport
  6. Sontrop, Herman M. J.; Moerland, Perry D.; Den Ham, René Van; Reinders, Marcel J. T.; Verhaegh, Wim F. J.: A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability (2009) ioport
  7. Harrar, Solomon W.; Gupta, Arjun K.: Asymptotic expansion for the null distribution of the (F)-statistic in one-way ANOVA under non-normality (2007)
  8. Turro, Ernest; Bochkina, Natalia; Hein, Anne-Mette K.; Richardson, Sylvia: BGX: a bioconductor package for the Bayesian integrated analysis of affymetrix genechips (2007) ioport
  9. Wu, Zhijin; Irizarry, Rafael A.: A statistical framework for the analysis of microarray probe-level data (2007)
  10. Dyson, Greg; Wu, C. F. Jeff: MAOSA: A new procedure for detection of differential gene expression (2006)