title: ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.
abstract: Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.
authors: Department of Informatics, University of Oslo, Oslo, Norway; Department of Tumour Biology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
authors: Halfdan
authors: H
authors: Rydbeck
authors: Department of Informatics, University of Oslo, Oslo, Norway.
authors: Geir Kjetil
authors: GK
authors: Sandve
authors: Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway; Science Institute, University of Iceland, Dunhaga 5, 107 Reykjavik, Iceland.
authors: Egil
authors: E
authors: Ferkingstad
authors: Department of Informatics, University of Oslo, Oslo, Norway.
authors: Boris
authors: B
authors: Simovski
authors: Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
authors: Morten
authors: M
authors: Rye
authors: Department of Informatics, University of Oslo, Oslo, Norway; Department of Tumour Biology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway; Department of Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
authors: Eivind
authors: E
authors: Hovig
mesh_terms: D016000:Cluster Analysis; D055106:Genome-Wide Association Study; D006801:Humans; D012984:Software