A Bayesian approach to estimation and testing in time-course microarray experiments

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.