GenomeAdapt (version 1.0.0)

AdmixProp: Estimate ancestral proportions from the eigen vectors

Description

Estimate ancestral (admixture) proportions based on the eigen-analysis

Usage

AdmixProp(x, groups, bound = FALSE)

Arguments

x

an object from GenomeAdapt

groups

A list of sample IDs, such like groups = list( CEU = c("NA0101", "NA1022", ...), YRI = c("NAxxxx", ...), Asia = c("NA1234", ...))

bound

if TRUE, the estimates are bounded so that no component < 0 or > 1, and the sum of proportions is one

Value

A dataframe of individual probability from the ancestries.

Details

Estimate ancestral (admixture) proportions based on the eigen-analysis, results give a dataframe of individual probability from the ancestries.

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.

Zheng, X., Levine, D., Shen, J., Gogarten, S. M., Laurie, C., & Weir, B. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326-3328.

Examples

# NOT RUN {
##---- Do genome scan and get the ancestry proportation for individuals ----
# Scan genomes (HapMap)

HapmapScan=GenomeAdapt.gds(genfile =SNPRelate::snpgdsExampleFileName(),
method="EIGMIX",num.thread = 1L, autosome.only=TRUE,
remove.monosnp=TRUE, maf=0.01, missing.rate=0.1)


# get population information

genofile <- SNPRelate::snpgdsOpen(SNPRelate::snpgdsExampleFileName())
pop_code <- gdsfmt::read.gdsn(gdsfmt::index.gdsn(genofile, "sample.annot/pop.group"))

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

# 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"))])

### estimate the ancestry proportion

Admixpro=AdmixProp(HapmapScan,groups=groups,bound=TRUE)

PlotAdmix(Admixpro,group=as.factor(pop_code),multiplot = FALSE)
# }