A permutation-based multiple testing method for time-course microarray experiments

• Published in: BMC bioinformatics Vol. 10; no. 1; p. 336
• Main Authors: Sohn, Insuk, Owzar, Kouros, George, Stephen L, Kim, Sujong, Jung, Sin-Ho
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.10.2009; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Computational biology; Computational Biology - methods; Databases, Factual; DNA microarrays; Gene expression; Gene Expression Profiling; Methodology; Methods; Oligonucleotide Array Sequence Analysis - methods; South Korea
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-10-336

Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation. In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey et al. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the Caenorhabditis elegans dauer developmental data. Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.