GhostKnockoff.prelim.Meta {GhostKnockoff}

Additional preliminary data management for GhostKnockoff if multiple studies are involved

Description

This function compute study dependency gamma and the optimal weights to combine multiple studies.

Usage

GhostKnockoff.prelim.Meta(cor.study, n.study)

Arguments

cor.study

The correlation among studies.

n.study

A vector of length K, where each element is the study sample size.

Value

w_opt

Optimal weights to combine multiple studies.

gamma

study dependency.

Examples

# We use genetic data as an example
library(GhostKnockoff)

# load example vcf file from package "seqminer", this serves as the reference panel
vcf.filename = system.file("vcf/1000g.phase1.20110521.CFH.var.anno.vcf.gz", package = "seqminer")

## this is how the actual genotype matrix from package "seqminer" looks like
example.G <- t(readVCFToMatrixByRange(vcf.filename, "1:196621007-196716634",annoType='')[[1]])
example.G <- example.G[,apply(example.G,2,sd)!=0]
example.G <- example.G[,1:100]

# compute correlation among variants
cor.G<-matrix(as.numeric(corpcor::cor.shrink(example.G)), nrow=ncol(example.G))

# fit null model
fit.prelim<-GhostKnockoff.prelim(cor.G,M=5,method='asdp',max.size=500)

# compute study correlation
Zscore_0<-cbind(rnorm(nrow(cor.G)),rnorm(nrow(cor.G))) # hypothetical Z-scores
Zscore_0<-Zscore_0+rbinom(nrow(cor.G),size=2,0.1) # set causal
cor.study<-GhostKnockoff.GetCorStudy(Zscore_0,fit.prelim)

# compute optimal weights and study dependency
n.study<-c(5000,7500)
Meta.prelim<-GhostKnockoff.prelim.Meta(cor.study, n.study)