ggrasp (version 1.2)

ggrasp.cluster: ggrasp.cluster

Description

ggrasp.cluster() clusters the genomes in a GGRaSP class variable and assigns the most representative genome in each cluster after accounting for rank as a medoid.

Usage

ggrasp.cluster(ggrasp.data, threshold, num.clusters, z.limit = 1,
  gmm.start = 2, gmm.max = 10, min.lambda = 0.005, run.type = "bgmm",
  left.dist = 1)

Arguments

ggrasp.data

Required. If neither a threshold or a num.cluster is given, a mixed model of Gaussian distributions is used to estimate a threshold to use the cluster.

threshold

The threshold used to cluster together all genomes within this distance.

num.clusters

Create this number of clusters independent of the cluster.

z.limit

All Gaussian distributions with means within this number of standard deviations will be reduced to only the larger distribution. Defaults to 1. Set to 0 to keep all non-overlapping distributions.

gmm.start

Number of Gaussian distributions to start the examination. Must be at least 2 and not greater than the gmm.max.

gmm.max

Maximum number of Gaussian distributions to examine. Has to be at least 2. 10 is the default

min.lambda

All Gaussian distributions with lambda value (proportion of the total distribution) below this value are removed before calculating the threshold. Default is 0.005. Set to 0 to keep all.

run.type

String giving the package to use to get the mixture model. Currently "bgmm" (default) and mixtools" are implemented.

left.dist

Number giving the number Gaussian distribution model immediately to the left of the threshold used. 1 is the default. Only value between 1 and k-1 where k is the total number of number of Gaussian distributions.

Value

Returns a class GGRaSP variable with the clusters and medoids assigned. In cases where the Gaussian Mixture Model was used to estimate the cutoff threshold, the descriptive values of the different distributions is also stored

Examples

# NOT RUN {
#The following data is from Chavda et al 2016 which phylotyped Enterobacter genomes
# Our example uses the data underpinning the tree shown in Figure 2
# Also included is a ranking file to prioritize closed Enterobactor genomes

#Loading the tree 
library(ggrasp);
tree.file <- system.file("extdata", "Enter.kSNP.tree", package="ggrasp")
rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp")
Enter.tree <- ggrasp.load(tree.file, file.format = "tree", rank.file = rank.file.in)

#Clustering the tree using a threshold estimated by Gaussian Mixture Models (GMMs)
# }
# NOT RUN {
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)
# }
# NOT RUN {

#Use print to get a list of the medoids selected
# }
# NOT RUN {
print(Enter.tree.cluster)
# }
# NOT RUN {
#Re-clustering the tree using a threshold estimated by the GMMs but without the distribution
#cleaning (i.e. removing the overlapping and low count distributions)
# }
# NOT RUN {
Enter.tree.reclust <- ggrasp.recluster(Enter.tree.cluster, z.limit=0, min.lambda = 0)
# }
# NOT RUN {
#Use plot to examine the tree with the clusters highlighted and the medoid genome names on the edge
# }
# NOT RUN {
plot(Enter.tree.cluster)
# }
# NOT RUN {
#Additional printing and plotting options are availible with plot() and print(). 
#For more information refer to ?plot.ggrasp and ?print.ggrasp
# }