A spline function approach for detecting differentially expressed genes in microarray data analysis | Zendy

Wenqing He | Zendy

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A spline function approach for detecting differentially expressed genes in microarray data analysis

Author(s) -

Wenqing He

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth339

Subject(s) - bayes' theorem , parametric statistics , computer science , microarray analysis techniques , statistical hypothesis testing , covariate , nonparametric statistics , bayes factor , parametric model , sample size determination , false discovery rate , inference , data mining , bayesian probability , statistics , mathematics , gene , machine learning , artificial intelligence , biology , genetics , gene expression

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.

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