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Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

Diboun, I; Wernisch, L; Orengo, CA; Koltzenburg, M; (2006) Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC GENOMICS , 7 , Article 252. 10.1186/1471-2164-7-252. Green open access

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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 10(e-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.

Type: Article
Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/1471-2164-7-252
Publisher version: http://dx.doi.org/10.1186/1471-2164-7-252
Language: English
Additional information: © 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.
Keywords: OLIGONUCLEOTIDE ARRAYS, SMALL SAMPLES, REPRODUCIBILITY, NEURONS
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/167939
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