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Create a minimum spanning network of selected populations using a distance matrix.
poppr.msn(gid, distmat, palette = topo.colors, mlg.compute = "original",
sublist = "All", blacklist = NULL, vertex.label = "MLG",
gscale = TRUE, glim = c(0, 0.8), gadj = 3, gweight = 1,
wscale = TRUE, showplot = TRUE, include.ties = FALSE,
threshold = NULL, clustering.algorithm = NULL, ...)
a distance matrix that has been derived from your data set.
a vector
or function
defining the color palette
to be used to color the populations on the graph. It defaults to
topo.colors
. See examples for details.
if the multilocus genotypes are set to "custom" (see
mll.custom
for details) in your genclone object, this will
specify which mlg level to calculate the nodes from. See details.
a vector
of population names or indexes that the user
wishes to keep. Default to "ALL".
a vector
of population names or indexes that the user
wishes to discard. Default to NULL
a vector
of characters to label each vertex. There
are two defaults: "MLG"
will label the nodes with the multilocus
genotype from the original data set and "inds"
will label the nodes
with the representative individual names.
"grey scale". If this is TRUE
, this will scale the color
of the edges proportional to the observed distance, with the lines becoming
darker for more related nodes. See greycurve
for details.
"grey adjust". a positive integer
greater than zero that
will serve as the exponent to the edge weight to scale the grey value to
represent that weight. See greycurve
for details.
"grey weight". an integer
. If it's 1, the grey scale
will be weighted to emphasize the differences between closely related
nodes. If it is 2, the grey scale will be weighted to emphasize the
differences between more distantly related nodes. See
greycurve
for details.
"width scale". If this is TRUE
, the edge widths will be
scaled proportional to the inverse of the observed distance , with the
lines becoming thicker for more related nodes.
logical. If TRUE
, the graph will be plotted. If
FALSE
, it will simply be returned.
logical. If TRUE
, the graph will include all edges
that were arbitrarily passed over in favor of another edge of equal weight.
If FALSE
, which is the default, one edge will be arbitrarily
selected when two or more edges are tied, resulting in a pure minimum
spanning network.
numeric. By default, this is NULL
, which will have no
effect. Any threshold value passed to this argument will be used in
mlg.filter
prior to creating the MSN. If you have a data set
that contains contracted MLGs, this argument will override the threshold in
the data set. See Details.
string. By default, this is NULL
. If
threshold = NULL
, this argument will have no effect. When supplied
with either "farthest_neighbor", "average_neighbor", or "nearest_neighbor",
it will be passed to mlg.filter
prior to creating the MSN. If
you have a data set that contains contracted MLGs, this argument will
override the algorithm in the data set. See Details.
any other arguments that could go into plot.igraph
a minimum spanning network with nodes corresponding to MLGs within the data set. Colors of the nodes represent population membership. Width and color of the edges represent distance.
a vector of the population names corresponding to the vertex colors
a vector of the hexadecimal representations of the colors used in the vertex colors
The minimum spanning network generated by this function is generated
via igraph's minimum.spanning.tree
. The resultant
graph produced can be plotted using igraph functions, or the entire object
can be plotted using the function plot_poppr_msn
, which will
give the user a scale bar and the option to layout your data.
The area of the nodes are representative of the number of samples. Because igraph scales nodes by radius, the node sizes in the graph are represented as the square root of the number of samples.
Each node on the graph represents a different multilocus genotype.
The edges on the graph represent genetic distances that connect the
multilocus genotypes. In genclone objects, it is possible to set the
multilocus genotypes to a custom definition. This creates a problem for
clone correction, however, as it is very possible to define custom lineages
that are not monophyletic. When clone correction is performed on these
definitions, information is lost from the graph. To circumvent this, The
clone correction will be done via the computed multilocus genotypes, either
"original" or "contracted". This is specified in the mlg.compute
argument, above.
If your incoming data set is of the class '>genclone
,
and it contains contracted multilocus genotypes, this function will retain
that information for creating the minimum spanning network. You can use the
arguments threshold
and clustering.algorithm
to change the
threshold or clustering algorithm used in the network. For example, if you
have a data set that has a threshold of 0.1 and you wish to have a minimum
spanning network without a threshold, you can simply add
threshold = 0.0
, and no clustering will happen.
The threshold
and clustering.algorithm
arguments can also be
used to filter un-contracted data sets.
All filtering will use the distance matrix supplied in the argument
distmat
.
plot_poppr_msn
nancycats
,
upgma
, nj
, nodelabels
,
tab
, missingno
, bruvo.msn
,
greycurve
# NOT RUN {
# Load the data set and calculate the distance matrix for all individuals.
data(Aeut)
A.dist <- diss.dist(Aeut)
# Graph it.
A.msn <- poppr.msn(Aeut, A.dist, gadj = 15, vertex.label = NA)
# Find the sizes of the nodes (number of individuals per MLL):
igraph::vertex_attr(A.msn$graph, "size")^2
# }
# NOT RUN {
# Set subpopulation structure.
Aeut.sub <- as.genclone(Aeut)
setPop(Aeut.sub) <- ~Pop/Subpop
# Plot respective to the subpopulation structure
As.msn <- poppr.msn(Aeut.sub, A.dist, gadj=15, vertex.label=NA)
# Show only the structure of the Athena population.
As.msn <- poppr.msn(Aeut.sub, A.dist, gadj=15, vertex.label=NA, sublist=1:10)
# Let's look at the structure of the microbov data set
library("igraph")
data(microbov)
micro.dist <- diss.dist(microbov, percent = TRUE)
micro.msn <- poppr.msn(microbov, micro.dist, vertex.label=NA)
# Let's plot it and show where individuals have < 15% of their genotypes
# different.
edge_weight <- E(micro.msn$graph)$weight
edge_labels <- ifelse(edge_weight < 0.15, round(edge_weight, 3), NA)
plot.igraph(micro.msn$graph, edge.label = edge_labels, vertex.size = 2,
edge.label.color = "red")
# }
# NOT RUN {
# }
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