poppr (version 2.5.0)

bruvo.msn: Create minimum spanning network of selected populations using Bruvo's distance.

Description

Create minimum spanning network of selected populations using Bruvo's distance.

Usage

bruvo.msn(gid, replen = 1, add = TRUE, loss = TRUE,
  mlg.compute = "original", palette = topo.colors, 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,
  ...)

Arguments

gid

a genind or genclone object

replen

a vector of integers indicating the length of the nucleotide repeats for each microsatellite locus.

add

if TRUE, genotypes with zero values will be treated under the genome addition model presented in Bruvo et al. 2004.

loss

if TRUE, genotypes with zero values will be treated under the genome loss model presented in Bruvo et al. 2004.

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.

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.

sublist

a vector of population names or indexes that the user wishes to keep. Default to "ALL".

blacklist

a vector of population names or indexes that the user wishes to discard. Default to NULL

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.

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.

References

Ruzica Bruvo, Nicolaas K. Michiels, Thomas G. D'Souza, and Hinrich Schulenburg. A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Molecular Ecology, 13(7):2101-2106, 2004.

See Also

bruvo.dist, nancycats, plot_poppr_msn, minimum.spanning.tree bruvo.boot, greycurve poppr.msn

Examples

# NOT RUN {
# Load the data set.
data(nancycats)

# View populations 8 and 9 with default colors. 
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
vertex.label.cex=0.7, vertex.label.dist=0.4)
# }
# NOT RUN {
# View heat colors.
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette=heat.colors, vertex.label.cex=0.7, vertex.label.dist=0.4)

# View custom colors. Here, we use black and orange.
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette = colorRampPalette(c("orange", "black")), vertex.label.cex=0.7, 
vertex.label.dist=0.4)

# View with darker shades of grey (setting the upper limit to 1/2 black 1/2 white).
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette = colorRampPalette(c("orange", "black")), vertex.label.cex=0.7, 
vertex.label.dist=0.4, glim=c(0, 0.5))

# View with no grey scaling.
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette = colorRampPalette(c("orange", "black")), vertex.label.cex=0.7, 
vertex.label.dist=0.4, gscale=FALSE)

# View with no line widths.
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette = colorRampPalette(c("orange", "black")), vertex.label.cex=0.7, 
vertex.label.dist=0.4, wscale=FALSE)

# View with no scaling at all.
bruvo.msn(nancycats, replen=rep(2, 9), sublist=8:9, vertex.label="inds", 
palette = colorRampPalette(c("orange", "black")), vertex.label.cex=0.7, 
vertex.label.dist=0.4, gscale=FALSE)

# View the whole population, but without labels.
bruvo.msn(nancycats, replen=rep(2, 9), vertex.label=NA)
# }