igm.get.pvalues: The permutation-based empirical NULL distribution of P-values...
In QQperm: Permutation Based QQ Plot and Inflation Factor Estimation

Description Usage Arguments Value Author(s) Examples

The permutation-based empirical NULL distribution of P-values is generated through label switching and permutation of the true case/control assignment. To achieve this, for a given matrix it randomly permutes the case and control labels of the original configuration and then recomputes the two-tail Fisher's Exact test for all genes. This is repeated n.permutation (e.g., 1000) times. For each of the n.permutations the p-values are ordered and then the mean of each rank-ordered estimate is taken across the n.permutations, i.e., the average 1st order statistic, the average 2nd order statistic, etc. These then represent the empirical estimates of the expected ordered p-values (expected -log10(p-values)). This empirical-based expected p-value distribution no longer depends on an assumption that the p-values are uniformly distributed under the null.

1	igm.get.pvalues(matrix, is.case, n.permutations = 1000)

`matrix`	The input genotype matrix, with rows for genes and columns for samples.
`is.case`	The case(1)/control(0) indicator.
`n.permutations`	The number of label permutations requested. Recommended number is 1,000.

Returns a list containing the observed P-values based on the correct case/control assignment (observed) and the permuted P-Values reflecting the appropriate NULL distribution for the given case/control configuration (perm).

Slave Petrovski and Quanli Wang