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


CONCLUSIONS
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.
Diboun
Ilhem
I
Bioinformatics Unit, Department of Biochemistry and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK. idiboun@biochem.ucl.ac.uk
Wernisch
Lorenz
L
Orengo
Christine Anne
CA
Koltzenburg
Martin
M
D000818:Animals; D001499:Bayes Theorem; D005727:Ganglia, Spinal / Q000378:metabolism; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016014:Linear Models; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D021141:Nucleic Acid Amplification Techniques / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012313:RNA / Q000235:genetics / Q000302:isolation & purification; D015203:Reproducibility of Results; D013116:Spinal Cord / Q000378:metabolism; D013223:Statistics as Topic / Q000379:methods*
D012313:RNA