Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data

Part of the Statistics for Biology and Health book series (SBH)


This chapter describes a collection of four R packages for exploratory analysis and normalization of two-color cDNA microarray fluorescence intensity data. R’s object-oriented class/method mechanism is exploited to allow efficient and systematic representation and manipulation of large microarray datasets of multiple types. The marrayClasses package contains class definitions and associated methods for pre- and postnormalization intensity data for batches of arrays. The marrayInput package provides functions and tcltk widgets to automate data input and the creation of microarray-specific R objects for storing these data. Functions for diagnostic plots of microarray spot statistics, such as boxplots, scatterplots, and spatial color images, are provided in marrayPlots. Finally, the marrayNorm package implements robust adaptive location and scale normalization procedures, which correct for different types of dye biases (e.g., intensity, spatial, plate biases) and allow the use of control sequences spotted onto the array and possibly spiked into the mRNA samples. The four new packages were developed as part of the Bioconductor project, which aims more generally to produce an open-source and open-development statistical computing framework for the analysis of genomic data.


Median Absolute Deviation Diagnostic Plot Color Palette Spot Statistic cDNA Microarray Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics 29:365–371.CrossRefGoogle Scholar
  2. Brown PO, Botstein D (1999). Exploring the new world of the genome with DNA microarrays. In: The Chipping Forecast, volume 21, 33–37. Supplement to Nature Genetics.Google Scholar
  3. Buckley MJ (2000). The Spot user’s guide. CSIRO Mathematical and Information Sciences, Sydney, Australia. // Scholar
  4. Cleveland WS (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74(368):829–836.CrossRefzbMATHMathSciNetGoogle Scholar
  5. Cleveland WS, Devlin SJ (1988). Locally-weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83:596–610.CrossRefzbMATHGoogle Scholar
  6. Dudoit S, Yang YH, Callow MJ, Speed TP (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experi-ments. Statistica Sinica 12(1):111–139.zbMATHMathSciNetGoogle Scholar
  7. Ihaka R, Gentleman R (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5:299–314.Google Scholar
  8. Leisch F (2002). Dynamic generation of statistical reports using literate data analysis. Technical Report 69, SFB Adaptive Information Systems and Modelling in Economics and Management Science, Vienna University of Economics and Business Administration: Vienna.Google Scholar
  9. Schena M (ed.) (2000). Microarray Biochip Technology. Eaton.Google Scholar
  10. Yang YH, Buckley MJ, Dudoit S, Speed TP (2002a). Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics 11(1):108–136.CrossRefMathSciNetGoogle Scholar
  11. Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP (2002b). Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30(4):e15.CrossRefGoogle Scholar
  12. Yang YH, Dudoit S, Luu P, Speed TP (2001). Normalization for cDNA microarray data. In: ML Bittner, Y Chen, AN Dorsel, ER Dougherty (eds.), Microarrays: Optical Technologies and Informatics, volume 4266 of Proceedings of SPIE, 141–152. SPIE: Bellingham, WA.Google Scholar

Copyright information

© Springer-Verlag New York, Inc. 2003

Authors and Affiliations

There are no affiliations available

Personalised recommendations