Authors

C. Angelini; D. De Canditiis; M. Mutarelli;M. Pensky

Comments

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Abbreviated Journal Title

Stat. Appl. Genet. Mol. Biol.

Keywords

DIFFERENTIALLY EXPRESSED GENES; FALSE DISCOVERY RATE; STATISTICAL-ANALYSIS; PROFILES; Biochemistry & Molecular Biology; Mathematical & Computational Biology; Statistics & Probability

Abstract

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data. The proposed procedure deals successfully with various technical difficulties that arise in typical microarray experiments such as a small number of observations, non-uniform sampling intervals and missing or replicated data. The procedure allows one to account for various types of errors and offers a good compromise between nonparametric techniques and techniques based on normality assumptions. In addition, all evaluations are performed using analytic expressions, so the entire procedure requires very small computational effort. The procedure is studied using both simulated and real data, and is compared with competitive recent approaches. Finally, the procedure is applied to a case study of a human breast cancer cell line stimulated with estrogen. We succeeded in finding new significant genes that were not marked in an earlier work on the same dataset.

Journal Title

Statistical Applications in Genetics and Molecular Biology

Volume

6

Publication Date

1-1-2007

Document Type

Article

Language

English

First Page

31

WOS Identifier

WOS:000249844800002

ISSN

1544-6115

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