![]() ![]() Permutation tests are frequently used for non-parametric testing and are incredibly valuable within computational biology, with applications within genome-wide association studies ( Browning, 2008 Dudbridge and Gusnanto, 2008 Purcell et al., 2007), Pathway Analysis ( Jeuken and Käll, 2018 Subramanian et al., 2005) and expression quantitative trait loci studies ( Doerge and Churchill, 1996 Sul et al., 2015). Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. ![]() Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. ![]()
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