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A Comparison of Parametric and Nonparametric Methods for Normalising cDNA Microarray Data
Author(s) -
Khondoker Mizanur R.,
Glasbey Chris A.,
Worton Bruce J.
Publication year - 2007
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200610338
Subject(s) - nonparametric statistics , statistics , computational biology , parametric statistics , microarray analysis techniques , mathematics , complementary dna , computer science , data mining , biology , econometrics , genetics , gene expression , gene
Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log‐ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics , 54 , 507–554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics , 18 , 96–104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)