Nucleic Acids Research Advance Access published online on April 25, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm093
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Methods Online |
An adaptation of the LMS method to determine expression variations in profiling data
1EMI 229 INSERM, Génotypes et Phénotypes Tumoraux, CRLC Val d’Aurelle-Paul Lamarque, Montpellier, France, 2Laboratoire de Biostatistique, Epidémiologie et Santé Publique, IURC, Université Montpellier I, Montpellier, France and 3Department of Immunology, Centro de Investigaciones Biológicas (CIB-CSIC), Ramiro de Maeztu 9, E-28040 Madrid, Spain
*To whom correspondence should be addressed. Tel: + 34918373112 ext. 4416; Fax: + 34915360432; Email: jasanz{at}cib.csic.es
Received October 11, 2006. Revised February 1, 2007. Accepted February 2, 2007.
One of the major issues in expression profiling analysis still is to outline proper thresholds to determine differential expression, while avoiding false positives. The problem being that the variance is inversely proportional to the log of signal intensities. Aiming to solve this issue, we describe a model, expression variation (EV), based on the LMS method, which allows data normalization and to construct confidence bands of gene expression, fitting cubic spline curves to the Box–Cox transformation. The confidence bands, fitted to the actual variance of the data, include the genes devoid of significant variation, and allow, based on the confidence bandwidth, to calculate EVs. Each outlier is positioned according to the dispersion space (DS) and a P-value is statistically calculated to determine EV. This model results in variance stabilization. Using two Affymetrix-generated datasets, the sets of differentially expressed genes selected using EV and other classical methods were compared. The analysis suggests that EV is more robust on variance stabilization and on selecting differential expression from both rare and strongly expressed genes.