Identification of Changes in Gene Expression

Evaluating the change in gene expression is a common goal in many research areas, such as in toxicological studies, which are particularly important in pre-clinical research. In practice, the analysis is often based on multiple t-tests evaluated at the observed time points of the experiment, which limits the accuracy of determining the precise time at which the gene changes in expression. If a parametric approach is chosen, the analysis is often restricted to identifying the onset of an effect, but not its length. In this paper, we propose a parametric method to identify the time frame during which the gene expression significantly changes. This is achieved by fitting a parametric model and constructing a confidence band for its first derivative. The confidence band is derived by a two-step bootstrap approach. It is summarized in terms of a hypothesis test, such that rejecting the null hypothesis means detecting a significant change in gene expression. Furthermore, a method for calculating confidence intervals for time points of interest (e.g., the beginning of significant change) is developed. We demonstrate the validity of our approach through a simulation study and present a variety of different applications to mouse gene expression data from a study investigating the effect of a Western diet on the progression of non-alcoholic fatty liver disease.