A comparison on effects of normalisations in the detection of differentially expressed genes.
BACKGROUND
Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.


RESULTS
Through three precise simulation plans, we quantify the impact of normalisations: (a) on the sensitivity and specificity of a specified test statistic for the identification of deregulated genes, (b) on the gene ranking induced by the statistic.


CONCLUSIONS
Although we found a limited difference of sensitivities and specificities for the test after each normalisation, the study highlights a strong impact in terms of gene ranking agreement, resulting in different levels of agreement between competing normalisations. However, we show that the combination of two normalisations, such as glog and lowess, that handle different aspects of microarray data, is able to outperform other individual techniques.
Chiogna
Monica
M
Department of Statistical Sciences, University of Padova, Padova, Italy. monica@stat.unipd.it
Massa
Maria Sofia
MS
Risso
Davide
D
Romualdi
Chiara
C
D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity