In order to account for the multiple testing problem caused by multiple SNPs within a gene, we performed 5,000 permutations, by randomly permuting the gene expression values and recomputing the association p-values between each SNP and the gene expression level.
Additionally, we tested a third permutation variant by randomly permuting the gene labels instead of the significance values of SNPs (row permutations II).
A permuted dataset is constructed by randomly permuting the class labels in the original gene expression dataset.
The permutation test was performed by randomly permuting the actual host species among the predictions obtained from STRUCTURE repeated 10,000 times.
To do so, we computed a background score distribution for each k-mer length by randomly permuting the intensity values and running Stable LASSO on the permuted values.
Specifically, R/MAANOVA recreates a null distribution of the data by randomly permuting the columns in the datasets in order to calculate the permutation-based p-value.
Then statistical significance for the weight value is computed by randomly permuting the samples into amplified and non-amplified groups and comparing the permuted weight value to the original.
For each of the eight time series, surrogates are created by randomly permuting the original time series.
The first process changes the position of vector data by randomly permuting the mean points of MCOs, the so-called position encryption.
Grangetto et al. [12] proposed a method in which the system modified the traditional arithmetic coder by randomly permuting the intervals in accordance with a key.
Here the importance of a predictor to the RFML model was assessed by randomly permuting the values of the factor within a validation dataset, and processing the validation data through the regression trees.
