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

Abstract

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.

4 Figures and Tables

Statistics

050100150'05'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

678 Citations

Semantic Scholar estimates that this publication has 678 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Pellis2003TheIC, title={The intraclass correlation coefficient applied for evaluation of data correction, labeling methods, and rectal biopsy sampling in DNA microarray experiments.}, author={Linette P Pellis and Nicole L W Franssen-van Hal and Jan Burema and Jaap Keijer}, journal={Physiological genomics}, year={2003}, volume={16 1}, pages={99-106} }