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Normalization for triple-target microarray experiments

BMC Bioinformatics
Springer (Biomed Central Ltd.)
Publication Date
DOI: 10.1186/1471-2105-9-216
  • Research Article
  • Computer Science
  • Design

Abstract ral ss BioMed CentBMC Bioinformatics Open AcceResearch article Normalization for triple-target microarray experiments Marie-Laure Martin-Magniette*1,2, Julie Aubert1, Avner Bar-Hen4, Samira Elftieh2, Frederic Magniette3, Jean-Pierre Renou2 and Jean- Jacques Daudin1 Address: 1UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France, 2UMR INRA 1165-CNRS 8114-UEVE URGV, 91057 Evry Cedex, France, 3Unité MOY300, Délégation CNRS île de France Est, 94532 Thiais Cedex, France and 4Universite Paris Descartes, MAP 5, Paris cedex 06, France Email: Marie-Laure Martin-Magniette* - [email protected]; Julie Aubert - [email protected]; Avner Bar- Hen - [email protected]; Samira Elftieh - [email protected]; Frederic Magniette - [email protected]; Jean- Pierre Renou - [email protected]; Jean-Jacques Daudin - [email protected] * Corresponding author Abstract Background: Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are Cy3 and Cy5. Recent improvements in the technology (dye-labelling, scanner and, image analysis) allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data. Results: We propose a two-step normalization procedure for triple-target experiments. Fi

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