BioNumerics
and GelCompar II Cluster Analysis Module
For an overview of BioNumerics and
comparison with GelCompar please see
BioNumerics.
Cluster analysis is now in widespread use
as a method of objectively determining the presence of groups of
similar patterns in a database. Cluster analysis may be applied to
sequence and phenotypic as well as gel data.
Consensus
clustering of two types of fingerprint patterns (click on
image for full size version)
Methods
- Creation
of dendrograms with similarity coefficients including product-moment Pearson correlation, cosine
correlation, Dice or Nei and Li, Jaccard, Jeffrey's X,
Ochia ,Gower, Canberra metric and Simple Matching. Area and exact
position matching measures are also available.
- Unweighted
pair-grouping (UPGMA), complete linkage (furthest
neighbor), single linkage (nearest neighbor), Ward or
Neighbor Joining clustering.
- Adjustable trace-to-trace
optimization and tolerance settings for banding patterns.
- Statistical determination of most justified tolerance
settings for banding patterns.
- Phylogenetic inference methods: Generalized
Parsimony, Maximum Likelihood.
Interpretation
- Combined display of character
images, sequences, normalized pattern images, with
similarity matrices and sorted according to
dendrogram(s).
- Indication of statistical error at all
linkage levels and calculation of co-phenetic
correlation.
- 'Seaweed' and pseudo-rooted
representation for unrooted trees. Bootstrap analysis for
single or composite datasets.
- Display of sorted
similarity matrices, shaded or with numerical similarity
values.
- Impressive edit and publishing functions.
-
Enhanced presentation and printing facilities, in a
WYSIWYG environment.
- Direct interaction between database
and dendrogram.
- Incremental and decremental
clustering: new entries can be added to or
deleted from existing cluster analyses, without having to
recalculate the complete analysis.
- All features of a
comparison are stored to disk.
Congruence between techniques
- Calculation of
global similarity or congruence between different
techniques as matrix or dendrogram.
- Easy visualization of
taxonomic depth or level of each technique by pairwise
regression plots of similarities.
Composite cluster analysis
- Different data sets of
the same type and of different types (fingerprint,
character, sequence and matrix) can be combined into one
consensus clustering.
- Calculation of global similarity by
merging characters or by averaging experiment-related
similarities.
- Optional weighting based on number of
characters or defined by the user.
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