makenull: Generate a mutual information null distribution from a gene expression matrix

Description

In order to determine the statistical significance of the mutual information values between genes we test for each pair of genes the following null hypothesis.

H_0^I: The mutual information between gene i and j is zero.

Because we are using a nonparametric test we need to obtain the corresponding null distribution for H_0^I from a randomization of the data.

'makenull' performs a randomization with the formulated null hypothesis by permuting the sample and gene labels for all genes of the entire expression matrix at once. The vector of the mutual information null distribution is obtained from repeated randomizations for a given number of iterations.

Usage

makenull(dataset, nullit=NA, estimator="pearson", disc="equalwidth")

Arguments

dataset

gene expression data matrix

nullit

nullit defines the size of the generated null distribution vector used for hypothesis testing of significant edges inferred by c3net. The null distribution of mutual information is generated from sample and gene label randomization.

default number of iterations: nullit=ceiling(10^5/(((genes*genes)/2)-genes)) genes: number of genes

estimator

estimators for continuous variables "pearson" (default), "spearman", "kendall", "spearman"

estimators for discrete variables "emp", "mm","sg","shrink"

disc

only required for discrete estimators, method for discretize function (infotheo package) "equalfreq" (default), "equalwidth", "globalequalwith"

Value

The function 'makenull' generates a numeric vector of the mutual information null distribution estimated from a given number of randomized datasets.

References

de Matos Simoes R, Emmert-Streib F., Bagging statistical network inference from large-scale gene expression data., PLoS One. 2012;7(3):e33624. Epub 2012 Mar 30.

Examples

Run this code

 data(expmat)
 null=makenull(expmat, nullit=5)