Abstract #302018

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JSM 2003 Abstract #302018
Activity Number: 451
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302018
Title: A Comparison of the Reproducibility of Gene Expression Data Obtained from Double and Single Label Hybridizations on cDNA Microarrays
Author(s): Jodi A. Lapidus*+ and Dongseok Choi+ and Jean O'Malley and Michael R. Lasarev and Xinfang Lu and Srinivasa Nagalla
Companies: Oregon Health and Science University and Oregon Health and Science University and Oregon Health and Science University and Oregon Health and Science University and Oregon Health and Science University and Oregon Health and Science University
Address: Dept. of Public Health & PM, Portland, OR, 97239, 3181 S.W. Sam Jackson Park Rd. CB-669, Portland, OR, 97239-2098,
Keywords: cDNA microarray ; single channel ; double channel ; normalization ; reproducibility
Abstract:

Spotted cDNA microarray experiments are a cost-effective way to study global changes in gene expression. A double-label technique has commonly been used to control for measurement error associated with variations in array processing and spot morphology. However, a systematic bias in measurement is introduced by the differences in chemical structure of the different labels. Although various normalization procedures have been proposed to correct for this bias as well as other potential biases, a better approach might be to use a single-label technique in which the hybridization of each sample is measured on a separate array. We will compare the reproducibility of the data obtained from double-label and the single-label microarrays and discuss an array normalization procedure for single-label experiments developed for this analysis. The datasets are from a recent microarray standardization experiment conducted by the Toxicogenomics Research Consortium of National Institute of Environmental Health Sciences to investigate methodology for the harmonization of microarray results from multiple laboratory sources using a variety of microarray performances.


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