title: MarVis: a tool for clustering and visualization of metabolic biomarkers.
abstract: BACKGROUND
  A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.
  
  
  RESULTS
  We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.
  
  
  CONCLUSIONS
  MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.
authors.affiliation: Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de
authors.forename: Alexander
authors.initials: A
authors.lastname: Kaever
authors.forename: Thomas
authors.initials: T
authors.lastname: Lingner
authors.forename: Kirstin
authors.initials: K
authors.lastname: Feussner
authors.forename: Cornelia
authors.initials: C
authors.lastname: Göbel
authors.forename: Ivo
authors.initials: I
authors.lastname: Feussner
authors.forename: Peter
authors.initials: P
authors.lastname: Meinicke
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software; D049490:Systems Biology; D014584:User-Computer Interface
chemical_list: D015415:Biomarkers