title: CLUE-TIPS, clustering methods for pattern analysis of LC-MS data.
abstract: Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.
authors.affiliation: Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, Massachusetts 02115, USA. lakella@mbl.edu
authors.forename: Lakshmi Manohar
authors.initials: LM
authors.lastname: Akella
authors.forename: Tomas
authors.initials: T
authors.lastname: Rejtar
authors.forename: Christina
authors.initials: C
authors.lastname: Orazine
authors.forename: Marina
authors.initials: M
authors.lastname: Hincapie
authors.forename: William S
authors.initials: WS
authors.lastname: Hancock
mesh_terms: D000465:Algorithms; D000818:Animals; D001943:Breast Neoplasms / Q000378:metabolism; D045744:Cell Line, Tumor; D002853:Chromatography, Liquid / Q000379:methods*; D016000:Cluster Analysis; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D009368:Neoplasm Transplantation; D011506:Proteins / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins