title: Clustering with position-specific constraints on variance: applying redescending M-estimators to label-free LC-MS data analysis.
abstract: BACKGROUND
  Clustering is a widely applicable pattern recognition method for discovering groups of similar observations in data. While there are a large variety of clustering algorithms, very few of these can enforce constraints on the variation of attributes for data points included in a given cluster. In particular, a clustering algorithm that can limit variation within a cluster according to that cluster's position (centroid location) can produce effective and optimal results in many important applications ranging from clustering of silicon pixels or calorimeter cells in high-energy physics to label-free liquid chromatography based mass spectrometry (LC-MS) data analysis in proteomics and metabolomics.
  
  
  RESULTS
  We present MEDEA (M-Estimator with DEterministic Annealing), an M-estimator based, new unsupervised algorithm that is designed to enforce position-specific constraints on variance during the clustering process. The utility of MEDEA is demonstrated by applying it to the problem of "peak matching"--identifying the common LC-MS peaks across multiple samples--in proteomic biomarker discovery. Using real-life datasets, we show that MEDEA not only outperforms current state-of-the-art model-based clustering methods, but also results in an implementation that is significantly more efficient, and hence applicable to much larger LC-MS data sets.
  
  
  CONCLUSIONS
  MEDEA is an effective and efficient solution to the problem of peak matching in label-free LC-MS data. The program implementing the MEDEA algorithm, including datasets, clustering results, and supplementary information is available from the author website at http://www.hephy.at/user/fru/medea/.
authors: Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria.
authors: Rudolf
authors: R
authors: Frühwirth
authors: D R
authors: DR
authors: Mani
authors: Saumyadipta
authors: S
authors: Pyne
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D002853:Chromatography, Liquid / Q000379:methods; D016000:Cluster Analysis; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics / Q000379:methods
chemical_list: D015415:Biomarkers