Cross phenotype normalization of microarray data

Jianhua Xuan, Yue Wang, Eric Hoffman, Robert Clarke

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normalization across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a comparison study of four normalization approaches, namely, linear regression (LR), Loess regression, invariant ranking (IR) and iterative nonlinear regression (INR) method, for gene expression. Among these four methods, LR and Loess regression methods use all available genes to estimate either a linear or nonlinear normalization function; while IR and INR methods feature some iterative processes to identify invariantly expressed genes (IEGs) for nonlinear normalization. We tested these normalization approaches on three real microarray data sets and evaluated their performance in terms of variance reduction and foldchange preservation. By comparison, we found that (1) LR method exhibits the worst performance in both variance reduction and fold-change preservation, and (2) INR method shows an improved performance in achieving low expression variance across replicates and excellent foldchange preservation for differently expressed genes.

Original languageEnglish (US)
Pages (from-to)171-186
Number of pages16
JournalFrontiers in Bioscience - Elite
Volume2 E
Issue number1
StatePublished - Jan 1 2010
Externally publishedYes


  • Computational bioinformatics
  • Gene expression profiling
  • Microarray data analysis
  • Nonlinear regression
  • Normalization


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