mutInfTest: Test for mutual information
In sigaR: Statistics for Integrative Genomics Analyses in R

Description Usage Arguments Value Author(s) References See Also Examples

A test evaluates the significance of the mutual information between two (high-dimensional) multivariate random variables. The null distribution is obtained via an efficient permutation resampling algorithm.

1 2	mutInfTest(Y, X, nPerm = 1000, method = "normal", k = 1, center = TRUE, rescale = TRUE, lowCiThres=0.10, ncpus=1, verbose=FALSE)

`Y`	(High-dimensional) matrix. Columns are assumed to represent the samples, and rows represent the samples' genes or traits.
`X`	(High-dimensional) matrix. Columns are assumed to represent the samples, and rows represent the samples' genes or traits. The number of columns of `X` must be identical to that of `Y`.
`nPerm`	Number of permutations.
`method`	Distributional assumption under which mutual information is to be estimated.
`k`	k-nearest neighbor parameter.
`center`	Logical indicator: should the rows of `Y` and `X` be centered at zero? Applied only under the normality assumption.
`rescale`	Logical indicator: should `Y` and `X` be rescaled to have the same scale? Applied only under the k-NN assumption.
`lowCiThres`	A value between 0 and 1. Determines speed of efficient p-value calculation. If the probability of a p-value being below `lowCiThres` is smaller than 0.001 (read: the test is unlikely to become significant), the permutation analysis is terminated and a p-value of 1.00 is reported.
`ncpus`	Number of cpus used for the permutations.
`verbose`	Logical indicator: should intermediate output be printed on the screen?

Object of miTest-class.

Wessel N. van Wieringen: w.vanwieringen@vumc.nl

Van Wieringen, W.N., Van der Vaart, A.W. (2011), "Statistical analysis of the cancer cell's molecular entropy using high-throughput data", Bioinformatics, 27(4), 556-563.

Van Wieringen, W.N., Van de Wiel, M.A., Van der Vaart, A.W. (2008), "A test for partial differential expression", Journal of the American Statistical Association, 103(483), 1039-1049.

hdMI

# load data
data(pollackCN16) 
data(pollackGE16) 
Y <- t(exprs(pollackGE16)) 
X <- t(copynumber(pollackCN16)) 

# perform testing and print test results
testRes <- mutInfTest(Y, X, nPerm = 1000) 
summary(testRes)