QPCorr.matrix {GEInter} | R Documentation |
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 |
E |
Input matrix of |
Y |
Response variable. A quantitative vector for |
tau |
Quantile. |
w |
Weight for accommodating censoring if |
family |
Response type of |
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
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")