BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

Background: The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for `systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dyeswaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and softw...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Mark J. Alston, John Seers, Jay C. D. Hinton, Sacha Lucchini
• Keywords: BABAR, BMCBI 2010, Microarray Data, Transcriptomic Datasets