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Open Access Research article

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

Robert R Kitchen123, Vicky S Sabine4, Arthur A Simen3, J Michael Dixon5, John MS Bartlett4 and Andrew H Sims1*

Author Affiliations

1 Applied Bioinformatics of Cancer Group, Breakthrough Breast Cancer Research Unit, Institute of Genetics and Molecular Medicine, Crewe Road South, Edinburgh, Edinburgh, EH4 2XR, UK

2 School of Physics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK

3 Yale University School of Medicine, Department of Psychiatry, 300 George Street, Suite 901, New Haven, CT 06511, USA

4 Endocrine Cancer Group, Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, Crewe Road South, Edinburgh, EH4 2XR, UK

5 Breast Cancer Research Group, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK

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BMC Genomics 2011, 12:589  doi:10.1186/1471-2164-12-589

Published: 1 December 2011

Abstract

Background

Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.

Results

A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependant upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.

Conclusions

The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.