An S4 class representing the GGRaSP data and output

Description

An S4 class representing the GGRaSP data and output

Slots

dist.mst

The distance matrix showing the distances between different genomes

phy

The phylogenetic tree in newick format

rank

The ranks of the respective genomes with lower getting higher priority in being called as a medoid

cluster

A vector giving the numeric cluster ID for each genome

h

The threshold variable used to make the clusters

medoids

A vector giving the medoid for each cluster

gmm

A data.frame containing all the gaussian distributions used to find the threshold when available

gmm.orig

A data.frame containing all the gaussian distributions prior to cleaning. Used to recalculate the threshold when needed

ggrasp.addRanks

Description

adds a rank file to a GGRaSP object. If clusters have been defined, the medoids will be re-defined

Usage

ggrasp.addRanks(x, rank.file)

Arguments

Value

A GGRaSP object where the ranks have been entirely redefined with the ranks in rank.file

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

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

#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)
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)


#Use print to get a list of the medoids selected
print(Enter.tree.cluster)

#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)
Enter.tree.reclust <- ggrasp.recluster(Enter.tree.cluster, z.limit=0, min.lambda = 0)

#Use plot to examine the tree with the clusters highlighted and the medoid genome names on the edge
plot(Enter.tree.cluster)

#Additional printing and plotting options are availible with plot() and print(). 
#For more information refer to ?plot.ggrasp and ?print.ggrasp

ggrasp.load

Description

ggrasp.load() initializes a class GGRaSP object from a file containing either a tree, a distance matrix or a multi-fasta alignment. The returned object can subsequently be clustered using ggrasp.cluster().

Usage

ggrasp.load(file, file.format, rank.file, offset, tree.method = "complete")

Arguments

Value

Returns a class GGRaSP variable

Examples

#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

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);

# Other options include loading by fasta file:
fasta.file <- system.file("extdata", "Enter.kSNP2.fasta", package="ggrasp")
rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp")
Enter.tree <- ggrasp.load(fasta.file, file.format = "fasta", rank.file =rank.file.in)

# and by distance matrix. Since this distance matrix is actually percent identity,
# we will us an offset of 100
mat.file <- system.file("extdata", "Enter.ANI.mat", package="ggrasp")
rank.file.in <- system.file("extdata", "Enter.kSNP.ranks", package="ggrasp")
Enter.in <- ggrasp.load(mat.file, file.format = "matrix", rank.file =rank.file.in, offset = 100)

# Use summary() to examine the data loaded
summary(Enter.in)

#Use plot() to see the tree
plot(Enter.in)

ggrasp.recluster

Description

recalculates a threshold and the resulting cluster using the previously defined Gaussian Mixture Model and provided threshold-determining factors. Requires the ggrasp.cluster to already have run

Usage

ggrasp.recluster(x, z.limit = 1, min.lambda = 0.005, left.dist = 1)

Arguments

Value

A GGRaSP object with the recalculated thresholds and the medoids using a previously generated GMM

Examples

#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

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

#Clustering the tree using a threshold estimated by Gaussian Mixture Models (GMMs)
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)


#Use print to get a list of the medoids selected
print(Enter.tree.cluster)

#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)
Enter.tree.reclust <- ggrasp.recluster(Enter.tree.cluster, z.limit=0, min.lambda = 0)

ggrasp.write

Description

writes formatted information from a class GGRaSP object to a file. Multiple output options are available.

Usage

ggrasp.write(x, file, type, rank.level)

Arguments

Examples

#Getting the ggrasp object
Enter.tree <- ggrasp.load(system.file("extdata", "Enter.kSNP.tree", package="ggrasp"), 
file.format = "tree", rank.file =system.file("extdata", "Enter.kSNP.ranks", package="ggrasp"));
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)

#Default examples: using the initizalized ggrasp object will 
#write the newick tree string to "tree.nwk"
ggrasp.write(Enter.tree, type="tree", file=file.path(tempdir(), "tree.nwk"));

# Using the clustered ggrasp object will write a text file with the clusters saved as an ITOL clade
# In conjecture with the phylogeny, this is readable by 
# ITOL web phylogeny visualizer (http://itol.embl.de/) 
ggrasp.write(Enter.tree.cluster, type="itol", file=file.path(tempdir(), "tree.itol.clade.txt"));

plot.ggrasp

Description

plots a class GGRaSP variable either the full tree, a reduced tree, or the gmm.

Usage

## S3 method for class 'ggrasp'
plot(x, type, layout = "circular", ...)

Arguments

Value

A ggplot object containing the plot. It can be printed to standard output or saved using ggsave.

print.ggrasp

Description

prints formatted information from a class GGRaSP object. Multiple output options are available.

Usage

## S3 method for class 'ggrasp'
print(x, type, rank.level, ...)

Arguments

Examples

#Getting the ggrasp object
Enter.tree <- ggrasp.load(system.file("extdata", "Enter.kSNP.tree", package="ggrasp"), 
file.format = "tree", rank.file =system.file("extdata", "Enter.kSNP.ranks", package="ggrasp"));
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)

#Default examples: using the initizalized ggrasp object will print the newick tree string 
print(Enter.tree);

# Using the clustered ggrasp object will print the medoids and their respective clusters
print(Enter.tree.cluster)
#Below are examples of using different output formats and rank levels
print(Enter.tree.cluster, "tree")
print(Enter.tree.cluster, "table", 1)
print(Enter.tree.cluster, "table", 0)

summary.ggrasp

Description

prints a summary of the class GGRaSP variable. Output includes medoids and cutoff value after the clustering

Usage

## S3 method for class 'ggrasp'
summary(object, ...)

Arguments