Permutation Test for CIBERSORT

Performs permutation-based sampling to generate an empirical null distribution of correlation coefficients for p-value calculation in CIBERSORT analysis. Randomly samples from the mixture data to create null distributions.

Usage

doPerm(perm, X, Y, absolute, abs_method, seed = NULL)

Arguments

perm: Integer. Number of permutations to perform (\(\ge\)100 recommended for reliable p-value estimation).
X: Matrix or data frame containing signature matrix (predictor variables).
Y: Numeric vector containing the mixture sample expression.
absolute: Logical indicating whether to use absolute space for weights.
abs_method: String specifying the method for absolute space weights: `"sig.score"` or `"no.sumto1"`.
seed: Integer. Random seed for reproducibility. If NULL (default), uses current random state.

Value

List containing:

dist: Numeric vector of correlation coefficients from permutations

Examples

# Simulate data
set.seed(123)
X <- matrix(rnorm(100), nrow = 10)
rownames(X) <- paste0("Gene", 1:10)
colnames(X) <- paste0("Cell", 1:10)
Y <- rnorm(10)
names(Y) <- paste0("Gene", 1:10)

# Run permutation test
result <- doPerm(10, X, Y, absolute = FALSE, abs_method = "sig.score")
if (!is.null(result)) str(result)
#> List of 1
#>  $ dist: num [1:10] 0.883 0.794 0.708 0.739 0.41 ...