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2009 BioSystematica

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.