title: Non-linear PCA: a missing data approach.
abstract: BACKGROUND
  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.
  
  
  CONCLUSIONS
  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.
  
  
  BACKGROUND
  scholz@mpimp-golm.mpg.de.
authors.affiliation: Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Scholz
authors.forename: Fatma
authors.initials: F
authors.lastname: Kaplan
authors.forename: Charles L
authors.initials: CL
authors.lastname: Guy
authors.forename: Joachim
authors.initials: J
authors.lastname: Kopka
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D017360:Arabidopsis / Q000502:physiology; D029681:Arabidopsis Proteins / Q000378:metabolism*; D003080:Cold Temperature; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant / Q000502:physiology; D008957:Models, Genetic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D015398:Signal Transduction / Q000502:physiology
chemical_list: D029681:Arabidopsis Proteins