GSAfisher.multiple: GSAfisher.multiple

In carlosgarciaprieto/GSAfishercombined: Gene Set Analysis with Fisher Combined Method

Description

Multiple method of GSAfisher function that computes the p-value for a joint test of association between a phenotype and multiple gene-sets of genetic variants (SNPs) by combining multiple marginal p-values data vectors using the Fisher method.

Usage

## S3 method for class 'multiple'
GSAfisher(...)

Arguments

...	Set of multiple marginal p-values data vectors or matrix.

Value

p.multiple

Set of multiple Fisher method combined p-values for each data vector.

Author(s)

Carlos Garcia Prieto

Examples

#Generate 3 vectors with 1000 random values from an Uniform distribution U (0, 0.25). 
data.vector1<-runif(1000, 0, 0.25)
data.vector2<-runif(1000, 0, 0.25)
data.vector3<-runif(1000, 0, 0.25)
#Set data class to "multiple".
class(data.vector1)<-"multiple"
#Apply the GSAfisher.multiple function to these data vectors.
GSAfisher(data.vector1, data.vector2, data.vector3)

#You can also try:
#Generate 5000 vectors with 1000 random values from an Uniform distribution U(0,0.25).
data.vectors<-sapply(1:5000, function (x) runif(1000, 0, 0.25))
#Set data class to "multiple".
class(data.vectors)<-"multiple"
#Apply the GSAfisher.multiple function to these data vectors.
GSAfisher(data.vectors)

carlosgarciaprieto/GSAfishercombined documentation built on May 14, 2019, 5:13 a.m.