A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings | Zendy

Aurora Torrente | Zendy; Misha Kapushesky | Zendy; Alvis Brāzma | Zendy

Open Access

A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings

Author(s) -

Aurora Torrente,

Misha Kapushesky,

Alvis Brāzma

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti644

Subject(s) - cluster analysis , hierarchical clustering , computer science , single linkage clustering , expression (computer science) , graph , data mining , correlation clustering , bipartite graph , algorithm , cure data clustering algorithm , pattern recognition (psychology) , artificial intelligence , theoretical computer science , programming language

Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly non-trivial to compare the results of a hierarchical and a flat, e.g. k-means, clustering.

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