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.
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.
Authors: Vedran Kajić; Marieh Esmaeelpour; Boris Považay; David Marshall; Paul L Rosin; Wolfgang Drexler Journal: Biomed Opt Express Date: 2011-12-12 Impact factor: 3.732
Authors: Julia Hofmann; Abd El Naser El Ashry; Shahbaz Anwar; Alexander Erban; Joachim Kopka; Florian Grundler Journal: Plant J Date: 2010-03-31 Impact factor: 6.417
Authors: Georgina Hernández; Oswaldo Valdés-López; Mario Ramírez; Nicolas Goffard; Georg Weiller; Rosaura Aparicio-Fabre; Sara Isabel Fuentes; Alexander Erban; Joachim Kopka; Michael K Udvardi; Carroll P Vance Journal: Plant Physiol Date: 2009-09-15 Impact factor: 8.340
Authors: Georgina Hernández; Mario Ramírez; Oswaldo Valdés-López; Mesfin Tesfaye; Michelle A Graham; Tomasz Czechowski; Armin Schlereth; Maren Wandrey; Alexander Erban; Foo Cheung; Hank C Wu; Miguel Lara; Christopher D Town; Joachim Kopka; Michael K Udvardi; Carroll P Vance Journal: Plant Physiol Date: 2007-04-20 Impact factor: 8.340