Non-linear PCA: a missing data approach.

Matthias Scholz; Fatma Kaplan; Charles L Guy; Joachim Kopka; Joachim Selbig

doi:10.1093/bioinformatics/bti634

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PubMed · 16109748

Non-linear PCA: a missing data approach.

Matthias Scholz·Fatma Kaplan·Charles L Guy·Joachim Kopka·Joachim Selbig

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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. CONTACT: scholz@mpimp-golm.mpg.de.

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Adaptation, Physiological Algorithms Arabidopsis Arabidopsis Proteins Cold Temperature Data Interpretation, Statistical Gene Expression Profiling Gene Expression Regulation, Plant Models, Genetic Models, Statistical Nonlinear Dynamics Oligonucleotide Array Sequence Analysis Principal Component Analysis Signal Transduction Journal Article Research Support, Non-U.S. Gov't

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Matthias Scholz, Fatma Kaplan, Charles L Guy, Joachim Kopka, Joachim Selbig. 2005-08-18. Non-linear PCA: a missing data approach.. https://doi.org/10.1093/bioinformatics%2Fbti634

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