Robust semiparametric microarray normalization and significance analysis

Shuangge Ma, Michael R. Kosorok, Jian Huang, Hehuang Xie, Liliana Manzella, Marcelo Bento Soares

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.
Original languageEnglish
Pages (from-to)555-561
Number of pages7
JournalBiometrics
Volume62
Issue number2
DOIs
Publication statusPublished - 1 Jun 2006
Externally publishedYes

Keywords

  • Normalization
  • Semiparametric model
  • Significance analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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