KCCA.test {GeneGeneInteR} | R Documentation

## KCCA (Kernel Canonical-Correlation Analysis) based GGI analysis.

### Description

`KCCA.test` performs a Gene-Gene Interaction (GGI) analysis based on the difference of canonical correlations between cases and controls. The "kernel trick" is applied to the canonical correlation to allow the detection of non-linear co-association.

### Usage

    KCCA.test(Y, G1, G2,kernel=c("rbfdot","polydot","tanhdot","vanilladot","laplacedot",
    "besseldot","anovadot","splinedot"),n.boot = 500,sigma=0.05,degree=1,scale=1,offset=1,
    order=1)

### 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`.
`kernel` | A character string matching one of the kernel name in : "rbfdot","polydot","tanhdot","vanilladot","laplacedot","besseldot","anovadot","splinedot". For more details regarding kernel function see `dots` object. Default is a Gaussian RBF kernel
`n.boot` | positive integer. `n.boot` is the number of bootstrap replicates for estimating variances. By default, this is fixed to 500.
`sigma` | The inverse kernel width used by the Gaussian the Laplacian (`kernel="rbfdot"`), the Bessel (`kernel="besseldot"`) and the ANOVA (`kernel="anovadot"`) kernel. Default is `sigma=0.05`.
`degree` | The degree of the polynomial (`kernel="polydot"`), bessel (`kernel="besseldot"`) or ANOVA (`kernel="anovadot"`) kernel function. This has to be an positive integer. Default is `degree=1`.
`scale` | The scaling parameter of the polynomial (`kernel="polydot"`) and tangent (`kernel="tanhdot"`) kernel is a convenient way of normalizing patterns without the need to modify the data itself. Default is `scale=1`.
`offset` | The offset used in a polynomial (`kernel="polydot"`) or hyperbolic tangent (`kernel="tanhdot"`) kernel. Default is `offset=1`.
`order` | The order of the Bessel function to be used as a kernel (`kernel="besseldot"`). Default is `order=1`.

### Details

The test statistic is based on the difference between a Fisher's transformation of the maximum of the kernelized canonical correlations in cases and controls. To calculate the test statistic for the interaction pvalue, `KCCA.test` estimates the variance of the Fisher's transformation of the maximum of the kernelized canonical correlations in cases and controls using a bootstrap method. The computation of `kcca.` can be very long.

### Value

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

`statistic` | The value of the statistic KCCU.
`p.value` | The p-value for the test.
`estimate` | A vector of the Fisher's transformed maximum kernel canonical correlation coefficient in cases and controls.
`parameter` | The number of boostrap samples used to estimate the p-value.
`null.value` | The value of KCCU under the null hypothesis.
`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

Yuan, Z. et al. (2012): Detection for gene-gene co-association via kernel canonical correlation analysis, BMC Genetics, 13, 83.
Larson, N. B. et al. (2013): A kernel regression approach to gene-gene interaction detection for case-control studies, Genetic Epidemiology, 37, 695-703.

### See Also

`GGI`, `CCA.test`

### Examples

    data(gene.pair)
    ## Not run:
    KCCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

    ## End(Not run)

[Package GeneGeneInteR version 1.12.0 Index]