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.