Comprehensive cluster analysis with Transitivity Clustering

Nat Protoc. 2011 Mar;6(3):285-95. doi: 10.1038/nprot.2010.197. Epub 2011 Feb 10.


Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis*
  • Computational Biology / methods*
  • Databases, Nucleic Acid
  • Databases, Protein
  • Gene Expression Profiling
  • Internet
  • Molecular Sequence Data
  • Pattern Recognition, Automated / methods*
  • Sequence Alignment / methods*
  • Sequence Analysis / methods
  • Sequence Homology
  • Software*
  • User-Computer Interface