Semiparametric RMA background-correction for oligonucleotide arrays

Ionut Bebu, Françoise Seillier-Moiseiwitsch, Hongfang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Microarray technology has provided an opportunity to simultaneously monitor the expression levels of a large number of genes in response to intentional perturbations. A necessary step towards successful use of microarray technology is background correction which aims to remove noise. One of the most popular algorithms for background correction is the Robust Multichip Average (RMA) procedure which relies on an unjustified parametric assumption. In this paper we first check the fitness of the RMA model using a graphical approach and then propose a new background correction method based on a semiparametric RMA model (semi-RMA). Evaluation of the proposed approach based on spike-in data and MAQC (Microarray Quality Control project) data shows our semi-RMA model provides a better fit to microarray data than other approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages1404-1408
Number of pages5
DOIs
StatePublished - 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: Jan 14 2007Jan 17 2007

Publication series

NameProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Other

Other7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Country/TerritoryUnited States
CityBoston, MA
Period1/14/071/17/07

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Bioengineering

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