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Bioinformatics Advance Access originally published online on August 18, 2005
Bioinformatics 2005 21(20):3887-3895; doi:10.1093/bioinformatics/bti634
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Non-linear PCA: a missing data approach

Matthias Scholz 1,*, Fatma Kaplan 2, Charles L. Guy 2, Joachim Kopka 1 and Joachim Selbig 1,3

1Max Planck Institute of Molecular Plant Physiology Potsdam, Germany
2University of Florida, Plant Molecular and Cellular Biology Program, Department of Environmental Horticulture Gainesville, Florida 32611, USA
3University of Potsdam Bioinformatics, Germany

*To whom correspondence should be addressed.

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.

Contact: scholz{at}mpimp-golm.mpg.de

Received on January 5, 2005; revised on August 2, 2005; accepted on August 15, 2005

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