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

BMC Bioinformatics
Mark J AlstonSacha Lucchini

Abstract

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, dye-swaps, 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 software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. The only input required by BABAR is unprocessed GenePix or...Continue Reading

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Citations

Mar 11, 2011·Current Opinion in Biotechnology·Magali HébrardJay C D Hinton
Apr 16, 2015·Journal of Experimental Botany·Rafael A CañasFrancisco M Cánovas
Oct 28, 2017·Environmental Microbiology·Georgios GiannopoulosDavid J Richardson
Nov 2, 2021·Frontiers in Veterinary Science·Emma SantosuossoFlorent David

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Datasets Mentioned

BETA
E-MEXP-1368
E-MEXP-2416

Methods Mentioned

BETA
gene array
gene

Software Mentioned

Windows XP
BlueFuse
Bioconductor
GenePix
MAList
QGEE
R
SIMAGE
limma
limma Linear Models for Microarray Analysis

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