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Non-linear PCA : a missing data approach

  • Motivation: Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values. Results: Here, we propose an inverse model that performs non-linear principal component analysis (NLPCA) from incomplete datasets. Missing values are ignored while optimizing the model, but can be estimated afterwards. Results are shown for both artificial and experimental datasets. In contrast to linear methods, non-linear methods were able to give better missing value estimations for non-linear structured data. Application: We applied this technique to a time course of metabolite data from a cold stress experiment on the model plant Arabidopsis thaliana, and could approximate the mapping function from any time point to the metabolite responses. Thus, the inverse NLPCA provides greatly improved information for better understanding the complex response to cold stress

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Metadaten
Author details:Matthias Scholz, F. Kaplan, C. L. Guy, Joachim KopkaORCiDGND, Joachim SelbigGND
ISSN:1367-4803
Publication type:Article
Language:English
Year of first publication:2005
Publication year:2005
Release date:2017/03/24
Source:Bioinformatics. - ISSN 1367-4803. - 21 (2005), 20, S. 3887 - 3895
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer review:Referiert
Institution name at the time of the publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik
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