English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/2877
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 10 veces en Web of Knowledge®  |  Pub MebCentral Ver citas en PubMed Central  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título : An adaptation of the LMS method to determine expression variations in profiling data
Autor : Chuchana, Paul; Marchand, Dorian; Nugoli, Mélanie; Rodríguez, Carmen; Molinari, Nicolas; García-Sanz, José A.
Fecha de publicación : 25-abr-2007
Editor: Oxford University Press
Citación : Nucleic Acids Research 2007 May; 35(9): e71.
PMCID: 1888829
Resumen: 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.
Descripción : The authors are indebted to Dr Irene Lopez-Vidriero (CNB-CSIC, Madrid) for help with the Latin square dataset analysis with MAS5. Drs Alain Henaut and Ulrich Mansmann for critical reading of the manuscript and useful propositions and comments. We would like to acknowledge Dr Andrew Kramar for his help in improving the English.
Versión del editor: http://dx.doi.org/doi:10.1093/nar/gkm093
URI : http://hdl.handle.net/10261/2877
DOI: 10.1093/nar/gkm093
Aparece en las colecciones: (CIB) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
anadaptation.pdf3,34 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.