Linked Life Data

15087312

Source:http://linkedlifedata.com/resource/pubmed/id/15087312

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15
pubmed:dateCreated
2004-10-12
pubmed:abstractText
MOTIVATION: Metabolite fingerprinting is a technology for providing information from spectra of total compositions of metabolites. Here, spectra acquisitions by microchip-based nanoflow-direct-infusion QTOF mass spectrometry, a simple and high throughput technique, is tested for its informative power. As a simple test case we are using Arabidopsis thaliana crosses. The question is how metabolite fingerprinting reflects the biological background. In many applications the classical principal component analysis (PCA) is used for detecting relevant information. Here a modern alternative is introduced-the independent component analysis (ICA). Due to its independence condition, ICA is more suitable for our questions than PCA. However, ICA has not been developed for a small number of high-dimensional samples, therefore a strategy is needed to overcome this limitation. RESULTS: To apply ICA successfully it is essential first to reduce the high dimension of the dataset, by using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The kurtosis measure is used to order the extracted components to our interest. Applied to our A. thaliana data, ICA detects three relevant factors, two biological and one technical, and clearly outperforms the PCA.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
12
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2447-54
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Metabolite fingerprinting: detecting biological features by independent component analysis.
pubmed:affiliation
Max Planck Institute of Molecular Plant Physiology, 14424 Potsdam, Germany. scholz@mpimp-golm.mpg.de
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies