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Deposited research article

A novel scheme to assess factors involved in the reproducibility of DNA-microarray data

Sacha AFT van Hijum¹, Anne de Jong¹, Richard JS Baerends¹, Harma A Karsens¹, Naomi E Kramer¹, Rasmus Larsen¹, Chris D den Hengst¹, Casper J Albers², Jan Kok¹ and Oscar P Kuipers¹

¹Department of Molecular Genetics, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, the Netherlands

²Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, the Netherlands

author email corresponding author email

Genome Biology 2005, 6:P4doi:10.1186/gb-2005-6-4-p4

Published:	3 March 2005

This was the first version of this article to be made available publicly.

Subject areas: Bioinformatics, Methods, Genome studies, Microbiology and parasitology

Abstract

Background

In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.

Results

A number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance.

Conclusions

Even in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from (very) lowly expressed genes. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.