Background#R##N#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.
['Benjamin M. Chain', 'Helen C. Bowen', 'John P. Hammond', 'Wilfried Posch', 'Jane Rasaiyaah', 'Jhen Tsang', 'Mahdad Noursadeghi']
Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays