A Permutation Based Multiple Testing method for Time Course Microarray Experiment

Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA.
BMC Bioinformatics (Impact Factor: 2.58). 10/2009; 10(1):336. DOI: 10.1186/1471-2105-10-336
Source: DBLP


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.

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    • "It is also of interest to compare the results from the proposed method with those of the EDGE method proposed by [12] and the permutation-based method by [20]. Since there are no longitudinal replicates available, the methods in [14] and [15] are not applicable. "
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    • "To estimate the empirical p-value for GI, 1000 permutations of the tissue samples were implemented and the importance values were recalculated for the permuted data set. The maximum Gini Index over all the genes in every permutation was recorded and thereby an empirical distribution of the maximum importance was estimated, as in similar analyses [49,50]. "
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    • "To balance the heavy computational burden and the size of the probability to be estimated, we performed 1000 replications that seemed large enough to estimate the empirical P-value at significance levels of 0.01 and 0.05 in simulation studies. Others have taken similar strategies in related simulations (McDonough et al., 2009; Sohn et al., 2009). In a real analysis, more permutations could be carried out if necessary. "
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