Visualizing estimated admixture proportions
Generally, users are most interested in the estimated admixture proportions for each sample. These are commonly visualized using STRUCTURE plots and pie plots. Functions for both are included in the package, and their use is detailed below.
STRUCTURE plots
Probably the most common method for visualizing admixture proportions
is using a stacked bar plot (commonly called a STRUCTURE plot after the
model-based clustering method STRUCTURE).
Users can generate a STRUCTURE plot for their data using the command
make.structure.plot, (see documentation at
help(make.structure.plot)). This function takes as its
principal argument the estimated admixture proportions and makes a
STRUCTURE plot in the plotting window. An example is given below.
First, we load the conStruct.results data output object
and, for convenience, assign the maximum a posteriori admixture
parameter estimates to a variable with a shorter name:
load("my_conStruct.results.Robj")
# assign the MAP admixture proportions from
# the first MCMC chain to a variable
# with a new name
admix.props <- conStruct.results$chain_1$MAP$admix.proportionsNow we can visualize the results:
# make a STRUCTURE plot using the
# maximum a posteriori (MAP) estimates
# from the first chain of a conStruct run
make.structure.plot(admix.proportions = admix.props)
Order STRUCTURE plots
The function also includes a variety of options for tweaking the order of the plotted samples.
# re-order the stacking order of the layers
make.structure.plot(admix.proportions = admix.props,
layer.order = c(2,1,3),
sort.by = 2)
# provide a custom sample ordering
# in this case by sample latitude
make.structure.plot(admix.proportions = admix.props,
sample.order = order(data.block$coords[,2]))
# add sample names
make.structure.plot(admix.proportions = admix.props,
sample.names = row.names(data.block$coords),
mar = c(4.5,4,2,2))
ADMIXTURE pie plots
It is often also useful to visualize estimated admixture proportions
in a spatial context by plotting them on a map. The most common way to
do this is to plot a pie plot at the sampling location of each sample,
in which each modeled layer gets its own slice of the pie
(K wedges), and the size of each slice in the pie is
proportional to the sample’s admixture proportion in that layer.
Users can make an admixture pie plot with their own data using the
command make.admix.pie.plot (see documentation at
help(make.admix.pie.plot). This function takes as its
principal arguments the estimated admixture proportions and the sample
coordinates, then makes an admixture pie plot in the plotting window. An
example is given below:
# make an admix pie plot using the
# maximum a posteriori (MAP) estimates
# from the first chain of a conStruct run
make.admix.pie.plot(admix.proportions = admix.props,
coords = data.block$coords)
# increase pie chart size
make.admix.pie.plot(admix.proportions = admix.props,
coords = data.block$coords,
radii = 4)
# zoom in on a subsection of the map
make.admix.pie.plot(admix.proportions = admix.props,
coords = data.block$coords,
x.lim = c(-130,-120),
y.lim = c(49,56))
Pie plot on a map
Users can also add the pie plot directly to a map of their own
creation using the make.admix.pie.plot by setting the
add argument to TRUE. E.g.,
# add pie plot to an existing map
# make the desired map
maps::map(xlim = range(data.block$coords[,1]) + c(-5,5), ylim = range(data.block$coords[,2])+c(-2,2), col="gray")
# add the admixture pie plot
make.admix.pie.plot(admix.proportions = admix.props,
coords = data.block$coords,
add = TRUE)