Open Access Research article

Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays

Benjamin Chain13*, Helen Bowen2, John Hammond2, Wilfried Posch1, Jane Rasaiyaah1, Jhen Tsang1 and Mahdad Noursadeghi1

Author Affiliations

1 Division of infection and Immunity, UCL, London, UK

2 Warwick HRI, University of Warwick, Warwick, UK

3 Windeyer Building, 46 Cleveland St., UCL,W1F 4JT, London, UK

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BMC Bioinformatics 2010, 11:344  doi:10.1186/1471-2105-11-344

Published: 24 June 2010

Abstract

Background

Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.

Results

We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.

Conclusions

This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.