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Physiol. Genomics 16: 99-106, 2003. First published October 21, 2003; doi:10.1152/physiolgenomics.00111.2003
1094-8341/03 $5.00
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Received 8 July 2003; accepted in final form 7 October 2003.
Physiological Genomics 16:99-106 (2003)
1094-8341/03 $5.00 © 2003 American Physiological Society

The intraclass correlation coefficient applied for evaluation of data correction, labeling methods, and rectal biopsy sampling in DNA microarray experiments

Linette Pellis1, Nicole L. W. Franssen-van Hal1, Jan Burema2 and Jaap Keijer1

1 Food Bioactives Group, RIKILT-Institute of Food Safety, 6700 AE Wageningen, The Netherlands
2 Division of Human Nutrition and Epidemiology, Wageningen University, 6700 EV Wageningen, The Netherlands

We show that the intraclass correlation coefficient (ICC) can be used as a relatively simple statistical measure to assess methodological and biological variation in DNA microarray analysis. The ICC is a measure that determines the reproducibility of a variable, which can easily be calculated from an ANOVA table. It is based on the assessment of both systematic deviation and random variation, and it facilitates comparison of multiple samples at once. We used the ICC first to optimize our microarray data normalization method and found that the use of median values instead of mean values improves data correction. Then the reproducibility of different labeling methods was evaluated, and labeling by indirect fluorescent dye incorporation appeared to be more reproducible than direct labeling. Finally, we determined optimal biopsy sampling by analyzing overall variation in gene expression. The variation in gene expression of rectal biopsies within persons decreased when two biopsies were taken instead of one, but it did not considerably improve when more than two biopsies were taken from one person, indicating that it is sufficient to use two biopsies per person for DNA microarray analysis under our experimental conditions. To optimize the accuracy of the microarray data, biopsies from at least six different persons should be used per group.

gene expression; data normalization method; between-person variation; within-person variation




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