Validate clusters in phylogenetic tree
LeafClusters
= cluster(Tree
, Threshold
)
[LeafClusters
, NodeClusters
]
= cluster(Tree
, Threshold
)
[LeafClusters
, NodeClusters
, Branches
]
= cluster(Tree
, Threshold
)
cluster(..., 'Criterion', CriterionValue
,
...)
cluster(..., 'MaxClust', MaxClustValue
,
...)
cluster(..., 'Distances', DistancesValue
,
...)
Tree  Phylogenetic tree object created, such as created with
the 
Threshold  Scalar specifying a threshold value. 
CriterionValue  Character vector or string specifying the criterion to determine the number of clusters as a function of the species pairwise distances. Choices are:

MaxClustValue  Positive integer specifying the maximum number of possible clusters for the tested partitions. Default is the number of leaves in the tree. TipWhen using the TipWhen using the 
DistancesValue  Matrix of pairwise distances, such as returned by the 
LeafClusters  Column vector containing a cluster index for each species
(leaf) in 
NodeClusters  Column vector containing the cluster index for each leaf
node and branch node in 
Branches  Twocolumn matrix containing, for each step in the algorithm, the index of the branch being considered and the value of the criterion. Each row corresponds to a step in the algorithm. The first column contains branch indices, and the second column contains criterion values. TipTo obtain the whole curve of the criterion versus the number
of clusters in 
returns
a column vector containing a cluster index for each species (leaf)
in a phylogenetic tree object. It determines the optimal number of
clusters as follows:LeafClusters
= cluster(Tree
, Threshold
)
Starting with two clusters (k = 2
),
selects the partition that optimizes the criterion specified by the 'Criterion'
property
Increments k by 1
and
again selects the optimal partition
Continues incrementing k and selecting
the optimal partition until a criterion value = Threshold
or k =
the maximum number of clusters (that is, number of leaves)
From all possible k values, selects the k value whose partition optimizes the criterion
[
returns
a column vector containing the cluster index for each leaf node and
branch node in LeafClusters
, NodeClusters
]
= cluster(Tree
, Threshold
)Tree
.
[
returns
a twocolumn matrix containing, for each step in the algorithm, the
index of the branch being considered and the value of the criterion.
Each row corresponds to a step in the algorithm. The first column
contains branch indices, and the second column contains criterion
values.LeafClusters
, NodeClusters
, Branches
]
= cluster(Tree
, Threshold
)
cluster(..., '
calls PropertyName
', PropertyValue
,
...)cluster
with optional properties
that use property name/property value pairs. You can specify one or
more properties in any order. Enclose each PropertyName
in
single quotation marks. Each PropertyName
is
case insensitive. These property name/property value pairs are as
follows:.
cluster(..., 'Criterion',
specifies the criterion to determine the number of
clusters as a function of the species pairwise distances. CriterionValue
,
...)
cluster(..., 'MaxClust',
specifies the maximum number of possible clusters
for the tested partitions. Default is the number of leaves in the
tree.MaxClustValue
,
...)
cluster(..., 'Distances',
substitutes the patristic distances in DistancesValue
,
...)Tree
with
a userprovided pairwise distance matrix.
Validate the clusters in a phylogenetic tree:
% Read sequences from a multiple alignment file into a MATLAB % structure gagaa = multialignread('aagag.aln'); % Build a phylogenetic tree from the sequences gag_tree = seqneighjoin(seqpdist(gagaa),'equivar',gagaa); % Validate the clusters in the tree and find the best partition % using the 'gain' criterion [i,j] = cluster(gag_tree,[],'criterion','gain','maxclust',10); % Use the returned vector of indices to color the branches of each % cluster in a plot of the tree h = plot(gag_tree); set(h.BranchLines(j==2),'Color','b') set(h.BranchLines(j==1),'Color','r')
[1] Dudoit, S. and Fridlyan, J. (2002). A predictionbased resampling method for estimating the number of clusters in a dataset. Genome Biology 3(7), research 0036.1–0036.21.
[2] Theodoridis, S. and Koutroumbas, K. (1999). Pattern Recognition (Academic Press), pp. 434–435.
[3] Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis (New York, Wiley).
[4] Calinski, R. and Harabasz, J. (1974). A dendrite method for cluster analysis. Commun Statistics 3, 1–27.
[5] Hartigan, J.A. (1985). Statistical theory in clustering. J Classification 2, 63–76.
cluster
 phytree
 phytreeread
 phytreeviewer
 plot
 seqlinkage
 seqneighjoin
 seqpdist
 silhouette
 view