Bootstrapping Time-Course Gene Expression Data for Gene Networks: Application to Gene Relevance Networks.

Identification of gene regulatory networks (GRNs) is a fundamental step to understand the molecular role of each gene and it helps to develop treatment and cure of a disease. To identify GRNs, time-course gene expression data are widely used. However, the identification is hampered by intrinsic attributes of the data such as small sample size, a large number of variables, and complex error structures with high variation. Under this situation, most GRN inference methods utilize point estimators or make numerous assumptions that are often incompatible with the experimental data. Moreover, different inference methods often provide inconsistent results. An alternative to alleviate this problem can be the bootstrap method because it provides more reliable outcomes by integrating results from multiple bootstrap samples without any distributional assumptions. In this study, we propose a bootstrap method for dependent time-course gene expression data and we mainly focus on its application to gene relevance networks. The proposed method is applied to gene networks for zebrafish retina.