PCA.test {GeneGeneInteR} | R Documentation

## PCA-based Gene-Gene Interaction analysis.

### Description

`PCA.test` performs a Gene-Gene Interaction (GGI) analysis by testing the interaction between the principal components of the two genes. With `method="Std"` PCA is standardized using standard deviation for each variable. With `method="GenFreq"`, dataset is standardized using the standard deviation under Hardy-Weinberg equilibrium, as proposed in the snpStats Bioconductor package.

### Usage

    PCA.test(Y, G1, G2, threshold = 0.8,method="GenFreq")

### 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`.
`threshold` | (optional) numeric comprised in [0, 1] interval. `threshold` is the minimum percentage of inertia kept for each PCA.
`method` | (optional) character string for PCA method. Must be one of the following: "GenFreq", "Std" (See details).

### Details

In a first step, `PCA.test` performs a Principal Components Analysis on both `G1` and `G2` genes that are interpreted as matrices of allele counts. With the `method="Std"`, the dataset is standardized using variables standard deviation, while dataset is standardized using standard deviation under Hardy-Weinberg equilibrium for `method="GenFreq"`. Principal components are then retrieved to describe each dataset with user-defined inertia percentage (parameter `threshold`) and used in a logistic regression model. The consists is testing the significance of the interaction terms using a Likelihood Ratio Test (see Li et al. (2009)).

### Value

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

`statistic` | The value of the statistic: the deviance of the anova test.
`p.value` | The p-value for the test.
`estimate` | A vector of the residual deviances.
`parameter` | The degrees of freedom of the chi-squared distribution of the test statistic.
`null.value` | the value of the deviance under the null.
`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. (2009) Identification of gene-gene interaction using principal components. BMC Proceedings, 3 (Suppl. 7): S78

### See Also

`GGI`

### Examples

    data(gene.pair)
    PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std")
    PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="GenFreq")

[Package GeneGeneInteR version 1.10.0 Index]