GenomeAdapt (version 1.0.0)

PlotAdmix: Plot Ancestry Admixture

Description

Plot admixture proportions based on their ancestries.

Usage

PlotAdmix(propmat, group = NULL, col = NULL,
multiplot = TRUE, xlab = "Individuals", ylab = "Ancestry Proportion",
showgrp = TRUE, shownum = TRUE, ylim = TRUE, na.rm = TRUE)

Arguments

propmat

a sample-by-ancestry matrix of proportion estimates, returned from AdmixProp()

group

a character vector of a factor according to the samples in propmat

col

specify colors

multiplot

single plot or multiple plots

xlab

The title for the x axis

ylab

The title for the y axis

showgrp

show group names in the plot

shownum

TRUE: show the number of each group in the figure

ylim

TRUE: y-axis is limited to [0, 1]; FALSE: ylim <- range(propmat); a 2-length numeric vector: ylim used in plot()

na.rm

TRUE: remove the sample(s) according to the missing value(s) in group

Value

Return to an anscrstry proportion plot/population structure plot that the same as STRUCTURE plot.

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

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)
# }