BMC Genomics Research article Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma Ilhem Diboun*1, Lorenz Wernisch2, Christine Anne Orengo1 and Martin Koltzenburg3 BioMed Central Open Access Address: 1Bioinformatics Unit, Department of Biochemistry and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK, 2School of Crystallography, Birkbeck College, University of London, Malet Street, London WC1 7HX, UK and 3Neural Plasticity Unit, UCL Institute of Child Health, 30 Guilford St, London WC1N 1EH, UK Email: Ilhem Diboun* - idiboun@biochem.ucl.ac.uk; Lorenz Wernisch - l.wernisch@mail.cryst.bbk.ac.uk; Christine Anne Orengo - orengo@biochem.ucl.ac.uk; Martin Koltzenburg - M.Koltzenburg@ich.ucl.ac.uk * Corresponding author Published: 09 October 2006 BMC Genomics 2006, 7:252 doi:10.1186/1471-2164-7-252 This article is available from: http://www.biomedcentral.com/1471-2164/7/252 Received: 01 September 2006 Accepted: 09 October 2006 © 2006 Diboun et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: RNA amplification is necessary for profiling gene expression from small tissue samples. Previous studies have shown that the T7 based amplification techniques are reproducible but may distort the true abundance of targets. However, the consequences of such distortions on the ability to detect biological variation in expression have not been explored sufficiently to define the true extent of usability and limitations of such amplification techniques. Results: We show that expression ratios are occasionally distorted by amplification using the Affymetrix small sample protocol version 2 due to a disproportional shift in intensity across biological samples. This occurs when a shift in one sample cannot be reflected in the other sample because the intensity would lie outside the dynamic range of the scanner. Interestingly, such distortions most commonly result in smaller ratios with the consequence of reducing the statistical significance of the ratios. This becomes more critical for less pronounced ratios where the evidence for differential expression is not strong. Indeed, statistical analysis by limma suggests that up to 87% of the genes with the largest and therefore most significant ratios (p < 10e-20) in the unamplified group have a p-value below 10e-20 in the amplified group. On the other hand, only 69% of the more moderate ratios (10e-20 < p < 10e-10) in the unamplified group have a p-value below 10e-10 in the amplified group. Our analysis also suggests that, overall, limma shows better overlap of genes found to be significant in the amplified and unamplified groups than the Z-scores statistics. Conclusion: We conclude that microarray analysis of amplified samples performs best at detecting differences in gene expression, when these are large and when limma statistics are used. Background Microarray technology offers a high throughput approach to transcriptional profiling on a genome wide scale. How- ever, the relatively large amount of starting material required for standard hybridization has limited its full potential. In complex biological systems such as the nerv- ous system, the utility of this approach is complicated by the fact that even in anatomically discrete regions, many divergent cell types are intermingled. It is often desirable to investigate gene expression profiles of distinct cell types Page 1 of 14 (page number not for citation purposes)