CLUE-TIPS, Clustering Methods for Pattern Analysis of LC−MS Data
| Autor: | Christina Orazine, Marina Hincapie, Tomas Rejtar, Lakshmi Manohar Akella, William S. Hancock |
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| Rok vydání: | 2009 |
| Předmět: |
Proteomics
Breast Neoplasms Sample (statistics) computer.software_genre Biochemistry Mass Spectrometry Set (abstract data type) Mice Similarity (network science) Cell Line Tumor Feature (machine learning) Animals Cluster Analysis Cluster analysis Mathematics business.industry Proteins Pattern recognition General Chemistry Hierarchical clustering Euclidean distance Data mining Artificial intelligence business computer Distance transform Algorithms Neoplasm Transplantation Chromatography Liquid |
| Zdroj: | Journal of Proteome Research. 8:4732-4742 |
| ISSN: | 1535-3907 1535-3893 |
| DOI: | 10.1021/pr900427q |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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