PLSPM.test: PLSPM (Partial Least Squares Path Modelling Analysis) based... In GeneGeneInteR: Tools for Testing Gene-Gene Interaction at the Gene Level

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

 1 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.

GGI
 1 2 data(gene.pair) PLSPM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair\$G2,n.perm=50)