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Cited by 592

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

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

Open access
Diboun, Ilhem, Wernisch, Lorenz, Orengo, Christine Anne, Koltzenburg, Martin·2006·BMC Genomics
DOI 10.1186/1471-2164-7-252
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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 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.

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Metadata

Authors
Diboun, I., Wernisch, L., Orengo, C. A., & Koltzenburg, M.
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2006-10-09
Volume
7
Issue
1
Pages
252-252
ISSN
1471-2164 (ISSN-L)
DOI
10.1186/1471-2164-7-252
OpenAlex
W2102395431
Cited by
592
References
15
FWCI
0.25
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. https://app.cochat.ai/papers/10.1186/1471-2164-7-252
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Diboun, I., Wernisch, L., Orengo, C. A., & Koltzenburg, M. (2006). Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. https://doi.org/10.1186/1471-2164-7-252

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