NMR metabolic analysis of samples using fuzzy K-means clustering

  1. (PDF, 434 KB)
  2. Get@NRC: NMR metabolic analysis of samples using fuzzy K-means clustering (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1002/mrc.2502
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
Journal titleMagnetic Resonance in Chemistry
PagesS96S104; # of pages: 8
Subjectfuzzy clustering; sample classification; metabolomics; metabolic profiling; mixture analysis; sample subtypes; 1H NMR; phenotype analysis
AbstractThe global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the applicationof fuzzy K-means clusteringmethod for the classificationof samples basedonmetabolomics 1D1HNMRfingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animalmodels. The cell line dataset includedNMRspectra of lipophilic cell extracts for twonormal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clusteringmethod allowedmore accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clusteringmethod.
Publication date
AffiliationNational Research Council Canada; NRC Institute for Information Technology; NRC Institute for Marine Biosciences
Peer reviewedYes
NPARC number16907865
Export citationExport as RIS
Report a correctionReport a correction
Record identifier2f352f7c-fc80-44dd-b8e2-cbe96fbcbfc7
Record created2011-02-22
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)
Date modified: