Create a minimum spanning network of selected populations using a distance matrix.

Usage

poppr.msn(
  gid,
  distmat,
  palette = topo.colors,
  mlg.compute = "original",
  sublist = "All",
  exclude = NULL,
  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,
  ...
)

Arguments

gid: a genind, genclone, genlight, or snpclone object
distmat: a distance matrix that has been derived from your data set.
palette: 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.
mlg.compute: 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.
sublist: a vector of population names or indexes that the user wishes to keep. Default to "ALL".
exclude: a vector of population names or indexes that the user wishes to discard. Default to NULL.
blacklist: DEPRECATED, use exclude.
vertex.label: 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.
gscale: "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.
glim: "grey limit". Two numbers between zero and one. They determine the upper and lower limits for the gray function. Default is 0 (black) and 0.8 (20% black). See greycurve for details.
gadj: "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.
gweight: "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.
wscale: "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.
showplot: logical. If TRUE, the graph will be plotted. If FALSE, it will simply be returned.
include.ties: 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.
threshold: 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.
clustering.algorithm: 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

Value

graph: 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.
populations: a vector of the population names corresponding to the vertex colors
colors: a vector of the hexadecimal representations of the colors used in the vertex colors

Details

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.

node sizes

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.

mlg.compute

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.

contracted multilocus genotypes

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.

Note

The edges of these graphs may cross each other if the graph becomes too large.

Author

Javier F. Tabima, Zhian N. Kamvar, Jonah C. Brooks

Examples


# 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
#>   [1]  2  1  2  2  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  9  1  1  5  1  1
#>  [26]  2  1  1  1  1  2  3  1  1  2  1  1  1  2  1  1  1  1  1  1  2  1  2  1  1
#>  [51]  1  2  1  3  1  1  1  1  1  2  1  1  1  2  1  1  1  1  1  1  1  1  1  2  1
#>  [76]  2  1  1 10  1  1  1  1  1  1  1  1  1  1  2  5  2  2  1  2  1  5  2  2  3
#> [101]  1  4  1  1  2  1  1  1  1  2  3  1  2  2  1  1  2  1  4

if (FALSE) { # \dontrun{
# 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")

} # }