Normalization of microarray data by iterative nonlinear regression

Jianhua Xuan, Eric Huffman, Robert Clarke, Yue Wang

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

4 Scopus citations

Abstract

Normalization is an important prerequisite for almost all follow-up microarray data analysis steps. Accurate normalization assures a common base for comparative biomedical studies using gene expression profiles across different experiments and phenotypes. In this paper, we present a novel normalization approach - iterative nonlinear regression (INR) method - that exploits concurrent identification of invariantly expressed genes (IEGs) and implementation of nonlinear regression normalization. We demonstrate the principle and performance of the INR approach on two real microarry data sets. As compared to major peer methods (e.g., linear regression method, Loess method and iterative ranking method), INR method shows a superior performance in achieving low expression variance across replicates and excellent fold change preservation.

Original languageEnglish (US)
Title of host publicationProceedings - BIBE 2005
Subtitle of host publication5th IEEE Symposium on Bioinformatics and Bioengineering
Pages267-270
Number of pages4
DOIs
StatePublished - 2005
Externally publishedYes
EventBIBE 2005: 5th IEEE Symposium on Bioinformatics and Bioengineering - Minneapolis, MN, United States
Duration: Oct 19 2005Oct 21 2005

Publication series

NameProceedings - BIBE 2005: 5th IEEE Symposium on Bioinformatics and Bioengineering
Volume2005

Conference

ConferenceBIBE 2005: 5th IEEE Symposium on Bioinformatics and Bioengineering
CountryUnited States
CityMinneapolis, MN
Period10/19/0510/21/05

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