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  Non-linear PCA: a missing data approach

Scholz, M., Kaplan, F., Guy, C. L., Kopka, J., & Selbig, J. (2005). Non-linear PCA: a missing data approach. Bioinformatics, 21(20), 3887-3895. doi:10.1093/bioinformatics/bti634.

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 Creators:
Scholz, M.1, Author              
Kaplan, F.2, Author
Guy, C. L.2, Author
Kopka, J.3, Author              
Selbig, J.1, Author              
Affiliations:
1BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              
2External Organizations, ou_persistent22              
3Applied Metabolome Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753338              

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Free keywords: neural-networks dimensionality reduction component analysis
 Abstract: 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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Language(s): eng - English
 Dates: 2005-08-202005
 Publication Status: Published in print
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 Identifiers: ISI: ISI:000232596300012
DOI: 10.1093/bioinformatics/bti634
ISSN: 1367-4803 (Print) 1367-4803 (Linking)
URI: ://000232596300012 http://bioinformatics.oxfordjournals.org/content/21/20/3887.full.pdf
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 21 (20) Sequence Number: - Start / End Page: 3887 - 3895 Identifier: -