pruneSNP {aSPU} | R Documentation |
When correlation matirx have highly correlated SNPs, the performance of aSPUs, aSPUsPath and MTaSPUsSet are not very good. Do pruning using this function.
pruneSNP(corSNP, rup = 0.95, rdown = -0.95)
corSNP |
Correlation matirx of the SNPs to be tested; estimated from a reference panel (based on the same set of the reference alleles as used in calculating Z-scores). |
rup |
pruning criteria, erase one SNP when correlation between two SNPs are larger than this value. |
rdown |
pruning criteria, erase one SNP when correlation between two SNPs are smaller than this value. |
a list object pruned.corSNP and to.erase. pruned.corSNP is pruned correlation matrix. to.erase is SNP index to erase to get purned object. (i.e. corSNP[-to.erase, -to.erase] = pruned.corSNP )
Il Youp Kwak
data(kegg9) ## example analysis using aSPUM test. g <- kegg9$gene.info[1,1] # SOAT1 ## Take snps mapped on gene "SOAT1" from the information of gene.info and snp.info. snps <- which( ( kegg9$snp.info[,2] == kegg9$gene.info[kegg9$gene.info[,1] == g, 2] ) & (kegg9$snp.info[,3] > kegg9$gene.info[kegg9$gene.info[,1] == g, 3] ) & (kegg9$snp.info[,3] < kegg9$gene.info[kegg9$gene.info[,1] == g, 4] ) ) ## Take subsets newP <- kegg9$nP[snps] ; ldsub <- kegg9$ldmatrix[snps, snps]; prSNP <- pruneSNP(ldsub) newP <- newP[-prSNP$to.erase] ldsub <- ldsub[-prSNP$to.erase, -prSNP$to.erase ] ## Get p-value for gene SOAT1. Read vignette for details. out <- aSPUs(newP, corSNP=ldsub , pow=c(1:8, Inf), n.perm=100, Ps=TRUE) out$Ts # This is a vector of Test Statistics for SPUM and aSPUM tests. # SPUs1 to SPUsInf corresponds with the option pow=c(1:8, Inf) # They are SPUs test statistics. # The last element aSPUs is minimum of them, aSPUs statistic. out$pvs # This is a vector of p-values for SPUs and aSPUs tests. # SPUs1 to SPUsInf corresponds with the option pow=c(1:8, Inf) # They are p-values for corresponding SPUs tests. # The last element is p-value of aSPUs test.