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Open Access Methodology article

Quality determination and the repair of poor quality spots in array experiments

Brian DM Tom1*, Walter R Gilks1, Elizabeth T Brooke-Powell23 and James W Ajioka2

Author Affiliations

1 Medical Research Council – Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK

2 Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK

3 Washington University, School of Medicine, Department of Molecular Microbiology, 660 S. Euclid Avenue, CB 8230, St. Louis, MO 63110, USA

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BMC Bioinformatics 2005, 6:234  doi:10.1186/1471-2105-6-234

Published: 26 September 2005

Abstract

Background

A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required.

Results

We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution – the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (HeLa).

Conclusion

As the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature.