Statistical Modelling 4 (2004), 195–209
Nonparametric analysis of replicated microarray experiments
Ali Gannoun,
Equipe de Probabilités et Statistique,
UMR CNRS 5149, CC 051,
Université Montpellier II,
F–34095 Montpellier Cedex 05,
France.
eMail: gannoun@math.univ.montp2.fr
Jérôme Saracco,
UMR CNRS 5149,
Université Montpellier II,
France.
Wolfgang Urfer
Department of Statistics,
University of Dortmund,
Germany.
George E. Bonney
Department of Microbiology and Statistical Genetics and Bioinformatics
Unit,
Howard University, National Human Genome Center,
Washington, DC,
USA.
Abstract:
Microarrays are part of a new class of biotechnologies, which
allow the monitoring of
expression levels of thousands of genes simultaneously. In
microarray data analysis, the comparison of
gene expression profiles with respect to different conditions
and the selection of biologically interesting
genes are crucial tasks. Multivariate statistical methods have
been applied to analyze these large data sets.
To identify genes with altered expression under two
experimental conditions, we propose a nonparametric
statistical approach. Specifically, we propose estimating
the distributions of a t-type statistic and its null
statistic, using kernel methods. A comparison of these two
distributions by means of a likelihood ratio test
can identify genes with significantly changed expressions.
A new method to provide more stable estimates
of tail probabilities is proposed, as well as a method for
the calculation of the cut-off point and the
acceptance region. The methodology is applied to a leukaemia
data set containing expression levels of 7129
genes, and is compared with normal mixture model and the
traditional t-test.
Keywords:
Kernel estimator; microarray; mixture modelling;
regression modelling; t-test
Downloads:
Data sets
and S-PLUS code in zipped archive
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