GBIGM.test: GBIGM (Gene-based information gain method) for GGI analysis.

Description Usage Arguments Details Value References See Also Examples

View source: R/GBIGM.R

Description

GBIGM.test performs a Gene-Gene Interaction (GGI) analysis by contrasting the information entropy between cases and controls.

Usage

1
GBIGM.test(Y, G1, G2, n.perm = 1000)

Arguments

Y

numeric or factor vector with exactly two different values. Y is the response variable and should be of length equal to the number of rows of G1 and G2 arguments (number of individuals).

G1

SnpMatrix object. Must have a number of rows equal to the length of Y.

G2

SnpMatrix object. Must have a number of rows equal to the length of Y.

n.perm

positive integer. n.perm is the number of permutations performed to compute the pvalue. By default, this is fixed to 1000.

Details

First, the conditional entropy and information gain rate are computed for each gene G1 and G2. In a second step, information gain rate for the gene pair (G1,G2) is computed. A p-value is estimated using permutations of Y. More details can be found in Li et al. (2015).

Value

A list with class "htest" containing the following components:

statistic

The value of the statistic DeltaR1,2.

p.value

The p-value for the test.

estimate

The estimation of DeltaR1,2.

parameter

The number of permutations used to estimate the p-value.

alternative

a character string describing the alternative.

method

a character string indicating the method used.

data.name

a character string giving the names of the data.

References

J. Li, et al.. A gene-based information gain method for detecting gene-gene interactions in case-control studies. European Journal of Human Genetics, 23 :1566-1572, 2015.

See Also

GGI

Examples

1
2
data(gene.pair)
GBIGM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=500)

MathieuEmily/GeneGeneInteR documentation built on May 7, 2019, 3:42 p.m.