NBF: Normal/Bayes factors method for finding associated pathways

In PAGWAS: Pathway Analysis Methods for Genomewide Association Data

Description

A vector of the computed Bayes factors for the tested pathways.

Usage

NBF(y, G, P, a, b, s2, nu)

Arguments

y	Response vector of length N
G	Genotype matrix, with N rows and L columns (number of tested SNPs)
P	Pathway matrix, with L columns and M columns (number of tested pathways)
a	Hyper-parameter of the variance assumed for the integrated out SNP effects
b	Hyper-parameter of the variance assumed for the pathway effects
s2	Hyper-parameter of the Inverse-Chi-squared distribution assumed for the variance of the response vector
nu	Hyper-parameter of the Inverse-Chi-squared distribution assumed for the variance of the response vector

Value

A vector of the computed Bayes factors of the same length as the number of tested pathways

References

Evangelou, M., Dudbridge, F., Wernisch, L. (2014). Two novel pathway analysis methods based on a hierarchical model. Bioinformatics, 30(5), 690 - 697.

Examples

## Not run: 
	data(genotypes)
	G=genotypes
	data(pathways)
	data(SNPs)
	data(genes)
	snps.genes=snps.to.genes(SNPs,genes,distance=0)
	snps.paths=snps.to.pathways(pathways,snps.genes)
	P=create.pathway.df(G,snps.paths)
	y=rnorm(nrow(G),mean=0,sd=10)
	NBF(y,G,P,a,b,s2,nu)
## End(Not run)

PAGWAS documentation built on May 2, 2019, 3:26 p.m.