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SNPRelate (version 1.6.4)

snpgdsEIGMIX: Eigen-analysis on SNP genotype data

Description

Eigen-analysis on IBD matrix based SNP genotypes.

Usage

snpgdsEIGMIX(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE, remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=1L, eigen.cnt=32L, need.ibdmat=FALSE, ibdmat.only=FALSE, verbose=TRUE)

Arguments

gdsobj
an object of class SNPGDSFileClass, a SNP GDS file
sample.id
a vector of sample id specifying selected samples; if NULL, all samples are used
snp.id
a vector of snp id specifying selected SNPs; if NULL, all SNPs are used
autosome.only
if TRUE, use autosomal SNPs only; if it is a numeric or character value, keep SNPs according to the specified chromosome
remove.monosnp
if TRUE, remove monomorphic SNPs
maf
to use the SNPs with ">= maf" only; if NaN, no MAF threshold
missing.rate
to use the SNPs with "
num.thread
the number of (CPU) cores used; if NA, detect the number of cores automatically
eigen.cnt
output the number of eigenvectors; if eigen.cnt
need.ibdmat
if TRUE, return the IBD matrix
ibdmat.only
return the IBD matrix only, do not compute the eigenvalues and eigenvectors
verbose
if TRUE, show information

Value

Return a snpgdsEigMixClass object, and it is a list:
sample.id
the sample ids used in the analysis
snp.id
the SNP ids used in the analysis
eigenval
eigenvalues
eigenvect
eigenvactors, "# of samples" x "eigen.cnt"
ibdmat
the IBD matrix

References

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2015 Oct 23. pii: S0040-5809(15)00089-1. doi: 10.1016/j.tpb.2015.09.004. [Epub ahead of print]

See Also

snpgdsEIGMIX, snpgdsPCA

Examples

Run this code
# open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())

# get population information
#   or pop_code <- scan("pop.txt", what=character())
#   if it is stored in a text file "pop.txt"
pop_code <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))

# get sample id
samp.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# run eigen-analysis
RV <- snpgdsEIGMIX(genofile)

# eigenvalues
RV$eigenval

# make a data.frame
tab <- data.frame(sample.id = samp.id, pop = factor(pop_code),
    EV1 = RV$eigenvect[,1],    # the first eigenvector
    EV2 = RV$eigenvect[,2],    # the second eigenvector
    stringsAsFactors = FALSE)
head(tab)

# draw
plot(tab$EV2, tab$EV1, col=as.integer(tab$pop),
    xlab="eigenvector 2", ylab="eigenvector 1")
legend("topleft", legend=levels(tab$pop), pch="o", col=1:4)


# define groups
groups <- list(CEU = samp.id[pop_code == "CEU"],
    YRI = samp.id[pop_code == "YRI"],
    CHB = samp.id[is.element(pop_code, c("HCB", "JPT"))])

prop <- snpgdsAdmixProp(RV, groups=groups)

# draw
plot(prop[, "YRI"], prop[, "CEU"], col=as.integer(tab$pop),
    xlab = "Admixture Proportion from YRI",
    ylab = "Admixture Proportion from CEU")
abline(v=0, col="gray25", lty=2)
abline(h=0, col="gray25", lty=2)
abline(a=1, b=-1, col="gray25", lty=2)
legend("topright", legend=levels(tab$pop), pch="o", col=1:4)



# run eigen-analysis
RV <- snpgdsEIGMIX(genofile, sample.id=samp.id[pop_code=="JPT"],
    need.ibdmat=TRUE)
z <- RV$ibdmat

mean(c(z))
mean(diag(z))


# close the genotype file
snpgdsClose(genofile)

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