### R code from vignette source 'ASGSCA.Rnw' ################################################### ### code chunk number 1: ASGSCA.Rnw:200-227 ################################################### library(ASGSCA) data("QCAHS") #Names of all the observed variables: the SNPs then the traits colnames(QCAHS) #Extract the variables of interest QCAHS1=data.frame(QCAHS$TaqIB,QCAHS$HindIII,QCAHS$G1302A,QCAHS$G1564A,QCAHS$G308A,QCAHS$G238A, QCAHS$HDL,QCAHS$LDL,QCAHS$APOB,QCAHS$TG,QCAHS$Glucose,QCAHS$Insulin) #Names of the observed variables used in this example ObservedVar=c("TaqIB","HindIII","G1302A","G1564A","G308A","G238A","HDL","LDL","APOB", "TG","Glucose","Insulin") colnames(QCAHS1)=ObservedVar #Define the vector of the latent variables names LatentVar=c("CETP","LPL","PGC","TNFa","Lipid metabolism","Energy metabolism") #Construction of the matrices W0 and B0 describing the model illustrated in Figure 2. W0=matrix(rep(0,12*6),nrow=12,ncol=6, dimnames=list(ObservedVar,LatentVar)) W0[1,1]=W0[2,2]=W0[3:4,3]=W0[5:6,4]=W0[7:10,5]=W0[8:12,6]=1 B0=matrix(rep(0,6*6),nrow=6,ncol=6, dimnames=list(LatentVar,LatentVar)) B0[1:3,5]=B0[3:4,6]=1 W0 B0 ################################################### ### code chunk number 2: ASGSCA.Rnw:235-236 ################################################### GSCA(QCAHS1,W0, B0,latent.names=LatentVar, estim=TRUE,path.test=FALSE,path=NULL,nperm=1000) ################################################### ### code chunk number 3: ASGSCA.Rnw:256-258 ################################################### set.seed(2) GSCA(QCAHS1,W0, B0,latent.names=LatentVar, estim=TRUE,path.test=TRUE,path=NULL,nperm=1000) ################################################### ### code chunk number 4: ASGSCA.Rnw:269-271 ################################################### set.seed(2) GSCA(QCAHS1,W0, B0,latent.names=LatentVar,estim=FALSE,path.test=TRUE,path=NULL,nperm=1000) ################################################### ### code chunk number 5: ASGSCA.Rnw:280-286 ################################################### set.seed(2) path0=matrix(c(2,3,5,6),ncol=2) path0 GSCA(QCAHS1,W0, B0,latent.names=LatentVar, estim=FALSE,path.test=TRUE,path=path0, nperm=1000) ################################################### ### code chunk number 6: ASGSCA.Rnw:297-309 ################################################### ObservedVar=colnames(QCAHS) ObservedVar #Define the vector of the latent variables names LatentVar=c("CETP","APOC3","ABCA1","FABP-2","APOA1","APOE","HL","LPL","MTP","PON1","PON2","PCSK9", "PGC","ADIPO","PPARg2","TNFa","eNOS","a23AR","b1AR","b2AR","b3AR","ACE","AGT","AGTR1","LEPR", "Lipid metabolism", "Energy metabolism","BP control") #The matrices W0 and B0 describing the model illustrated in Figure 2. data(W0); data(B0) dim(W0) dim(B0) ################################################### ### code chunk number 7: ASGSCA.Rnw:326-336 ################################################### #set.seed(4) #ResQCAHS=GSCA(QCAHS,W0, B0,latent.names=LatentVar, estim=TRUE,path.test=TRUE,path=NULL,nperm=1000) data("ResQCAHS") indices <- which(ResQCAHS$pvalues<0.05, arr.ind=TRUE) ind.row=indices[,1] ; ind.col=indices[,2] Significant<- matrix(rep(0,nrow(indices)*3),ncol=3);colnames(Significant)=c("Gene", "Pathway", "pval") Significant[,1] <- rownames(ResQCAHS$pvalues)[ind.row] Significant[,2] <- colnames(ResQCAHS$pvalues)[ind.col] Significant[,3]<-ResQCAHS$pvalues[indices] Significant