title: pyGCluster, a novel hierarchical clustering approach.
abstract: CONCLUSIONS
  pyGCluster is a clustering algorithm focusing on noise injection for subsequent cluster validation. The reproducibility of a large amount of clusters obtained with agglomerative hierarchical clustering is assessed. Furthermore, a multitude of different distance-linkage combinations are evaluated. Finally, highly reproducible clusters are meta-clustered into communities. Graphical illustration of the results as node and expression maps is implemented.
  
  
  METHODS
  pyGCluster requires Python 2.7, it is freely available at http://pyGCluster.github.io and published under MIT license. Dependencies are NumPy, SciPy and optionally fastcluster and rpy2.
  
  
  BACKGROUND
  christan@fufezan.net
  
  
  BACKGROUND
  Supplementary data is available at Bioinformatics online and at http://pyGCluster.github.io.
authors.affiliation: Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Jaeger
authors.forename: Johannes
authors.initials: J
authors.lastname: Barth
authors.forename: Anna
authors.initials: A
authors.lastname: Niehues
authors.forename: Christian
authors.initials: C
authors.lastname: Fufezan
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results