GEInter (version 0.3.2)

QPCorr.matrix: Robust identification of gene-environment interactions using a quantile partial correlation approach

Description

A robust gene-environment interaction identification approach using the quantile partial correlation technique. This approach is a marginal analysis approach built on the quantile regression technique, which can accommodate long-tailed or contaminated outcomes. For response with right censoring, Kaplan-Meier (KM) estimator-based weights are adopted to easily accommodate censoring. In addition, it adopts partial correlation to identify important interactions while properly controlling for the main genetic (G) and environmental (E) effects.

Usage

QPCorr.matrix(G, E, Y, tau, w = NULL, family = c("continuous", "survival"))

Arguments

G

Input matrix of p G measurements consisting of n rows. Each row is an observation vector.

E

Input matrix of q E risk factors. Each row is an observation vector.

Y

Response variable. A quantitative vector for family="continuous". For family="survival", Y should be a two-column matrix with the first column being the log(survival time) and the second column being the censoring indicator. The indicator is a binary variable, with "1" indicating dead, and "0" indicating right censored.

tau

Quantile.

w

Weight for accommodating censoring if family="survival". Default is NULL and a Kaplan-Meier estimator-based weight is used.

family

Response type of Y (see above).

Value

Matrix of (censored) quantile partial correlations for interactions.

References

Yaqing Xu, Mengyun Wu, Qingzhao Zhang, and Shuangge Ma. Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach. Genomics, 111(5):1115-1123, 2019.

See Also

QPCorr.pval method.

Examples

# NOT RUN {
alpha=matrix(0,5,1)
alpha[1:2]=1
beta=matrix(0,6,100)
beta[1,1:5]=1
beta[2:3,1:5]=2
beta[4:6,6:7]=2
sigmaG<-AR(rho=0.3,100)
sigmaE<-AR(rho=0.3,5)
G<-MASS::mvrnorm(200,rep(0,100),sigmaG)
E<-MASS::mvrnorm(200,rep(0,5),sigmaE)
e1<-rnorm(200*.05,50,1);e2<-rnorm(200*.05,-50,1);e3<-rnorm(200*.9)
e<-c(e1,e2,e3)

# continuous
y1=simulated_data(G=G,E=E,alpha=alpha,beta=beta,error=e,family="continuous")
cpqcorr_stat1<-QPCorr.matrix(G,E,y1,tau=0.5,w=NULL,family="continuous")

# survival
y2=simulated_data(G,E,alpha,beta,rnorm(200,0,1),family="survival",0.7,0.9)
cpqcorr_stat<-QPCorr.matrix(G,E,y2,tau=0.5,w=NULL,family="survival")

# }