BioNumerics
and GelCompar II Cluster Analysis Module
For an overview 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 including up to 10,000 database entries using
product-moment Pearson correlation, cosine correlation, Dice or Nei
and Li, Jaccard, Jeffrey's X, Ochiai and area sensitive relatives for
banding patterns, Gower, Canberra metric, Simple Matching, etc.
Categorical coefficient for multi-state character data such as MLST or
VNTR. 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. Population modelling: Analysis of
categorical data such as MLST or VNTR (MLVA) using Minimum Spanning
Trees to reconstruct evolutionary models. Advanced presentation
and editing tools.
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. Comprehensive edit and publishing functions. Professional
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.
Plots and graphs. Creation of 2-D and 3-D bar graphs,
contingency tables, 2-D and spatial 3-D scatterplots or feature plots
from database fields and characters. Professional presentation,
printing and exporting tools.
For further details please download
the BioNumerics/GelCompar pdf brochure.
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