MantelCorr (version 1.42.0)

PermutatonTest: Permutation Test for Dissimilarity Matrices

Description

'PermutationTest' computes and returns an empirical p-value from a null distribution generated by permuting 'Dfull' a total of 'num.per' times.

Usage

PermutationTest(Dfull, Dsubsets, num.per, num.chips, alpha)

Arguments

Dfull

dissimilarity matrix from the original (p x n) microarray expression data

Dsubsets

dissimilarity matrices from each k disjoint clusters returned by 'GetClusters'

num.per

number of permutations

num.chips

number of samples, 'n' from the original (p x n) data matrix

alpha

desired level of significance

Value

returns the permuted p-value for the 'alpha' selected level of significance

Warning

(p x n) data matrix should be numeric (e.g. gene-expression levels)

Details

For each permutation, k Mantel correlations are computed by correlating the permuted 'Dfull' with each dissimilarity matrix 'Dsubsets' from the 'k' clusters returned by 'GetClusters'. The absolute value of the maximum Mantel cluster correlation is retained at each permutation. These 'num.per' maximum correlations are then used to generate a null distribution for distance metric independence, with the p-value taken from the (1 - 'alpha') percentile of this permutation distribution.

See Also

'GetClusters' 'DistMatrices' 'MantelCorrs'

Examples

# simulate a p x n microarray expression dataset, where p = genes and n = samples
data.sep <- rbind(matrix(rnorm(1000), ncol=50), matrix(rnorm(1000, mean=5), ncol=50))
noise <- matrix(runif(40000), ncol=1000)
data <- t(cbind(data.sep, noise))
data <- data[1:200, ]
# data has p = 1,050 genes and n = 40 samples

clusters.result <- GetClusters(data, 100, 100)
dist.matrices <- DistMatrices(data, clusters.result$clusters)
mantel.corrs <- MantelCorrs(dist.matrices$Dfull, dist.matrices$Dsubsets)
permutation.result <- PermutationTest(dist.matrices$Dfull, dist.matrices$Dsubsets, 100, 40, 0.05)