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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.