Methodology article

A methodology for global validation of microarray experiments

Mathieu Miron¹ , Owen Z Woody¹ , Alexandre Marcil¹ , Carl Murie¹ , Robert Sladek¹ and Robert Nadon^1,2

¹  McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada

²  McGill University Department of Human Genetics, 1205 avenue du Docteur Penfield N5/13, Montreal, Quebec, H3A 1B1, Canada

author email corresponding author email

BMC Bioinformatics 2006, 7:333doi:10.1186/1471-2105-7-333

Published:	5 July 2006

Abstract

Background

DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies.

Results

We present an approach for the global validation of DNA microarray experiments that will allow researchers to evaluate the general quality of their experiment and to extrapolate validation results of a subset of genes to the remaining non-validated genes. We illustrate why the popular strategy of selecting only the most differentially expressed genes for validation generally fails as a global validation strategy and propose random-stratified sampling as a better gene selection method. We also illustrate shortcomings of often-used validation indices such as overlap of significant effects and the correlation coefficient and recommend the concordance correlation coefficient (CCC) as an alternative.

Conclusion

We provide recommendations that will enhance validity checks of microarray experiments while minimizing the need to run a large number of labour-intensive individual validation assays.