PLSPM.test {GeneGeneInteR} | R Documentation

## PLSPM (Partial Least Squares Path Modelling Analysis) based Gene-Gene Interaction analysis.

### Description

`PLSPM.test` performs a Gene-Gene Interaction (GGI) analysis based on the modelisation of a netwrok of statistical relations. The aim is to quantify the connections between the latent and the manifest variables.

### Usage

    PLSPM.test(Y, G1, G2,n.perm=500)

### 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 500.

### Details

The PLSPM-based method, as described in Zhang et al. (2013), aims at comparing the path coefficients β_D and β_C, where β_D is calculated among cases and β_C among controls. The co-association between genes `G1` and `G2` is defined by:

U=\frac{β_D-β_C}{√{Var(β_D-β_C)}}.

The `plspm` R package is used to estimate U. The significance pvalue is obtained by using a permutation method on the difference between the path coefficients.

### Value

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

`statistic` | The value of the statistic U.
`p.value` | The p-value for the test.
`estimate` | A vector of the path coefficients in cases and controls.
`parameter` | The number of boostrap samples used to estimate the p-value.
`null.value` | The value of U 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

X. Zhang et al. (2013) A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design. PLoS ONE, 8(4) :e62129.

### See Also

`GGI`

### Examples

    data(gene.pair)
    PLSPM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=50)

[Package GeneGeneInteR version 1.6.0 Index]