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We use the permutation test to evaluate the significance of the correlation and set the p-value threshold as 0.05.
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We used the permutation test to determine the optimal threshold of the PCC for gene co-expression analysis (Ouyang et al. 2012).
For each of the 1000 cases, the rotational symmetries of the two data sets were compared using the permutation test with 1000 permutations per test, and the power was calculated as the fraction of times that the permutation test correctly rejected the null hypothesis of similar symmetries (using a significance level of 0.05).
Using the permutation test method, with 10,000 permutations, we found 10 miRNAs were strongly correlated with survival (p≤0.005).
The relationship between body-shape and swimming performance was tested by comparing the variable vector with the shape vector, using the permutation test available in TpsPls.
To establish differences in gene expression between tissue groups, both gene-by-gene statistical testing using the permutation test with t statistics and singular value decomposition (SVD) of the data matrix were employed.
In addition, the possibility of over-fitting was tested using the permutation test [ 11, 12, 15].
To address the issue of multiple testing, we used the permutation test in R[ 20].
We used the permutation test (1000 permutations) to estimate P-values.
By using the permutation test we made no assumption about the distribution under the null hypothesis.
The threshold at 5% significance level was estimated using the permutation test implemented in R/qtl.
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