Bioinformatics Advance Access originally published online on June 4, 2004
Bioinformatics 2004 20(17):2954-2963; doi:10.1093/bioinformatics/bth339
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
A spline function approach for detecting differentially expressed genes in microarray data analysis
Prossermen Center for Health Research, Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada M5G 1X5
Received on December 17, 2003; revised on May 17, 2004; accepted on May 21, 2004
Advance Access Publication June 4, 2004
Motivation: A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as empirical Bayes methods and mixture normal approaches, have been proposed, but the inferences are complicated and the tests may not have as much power as parametric models.
Results: We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes. Standard maximum likelihood methods can be employed to make inferences. For illustration purposes the proposed method is applied to the leukemia data (training part) discussed elsewhere. A simulation study is conducted to evaluate the performance of the proposed method.
Contact: he{at}mshri.on.ca
* Present address: Department of Statistics and Actuarial Sciences, University of Western Ontario, 1151 Richmond Street North, London, Ontario, Canada N6A 5B7
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