AVEDA: Statistical Tests for Finding Interesting Visualisations
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Analysing periodic phenomena by circular PCA
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Imputation of missing values for compositional data using classical and robust methods
Computational Statistics & Data Analysis
EURASIP Journal on Bioinformatics and Systems Biology
Visualisation of test coverage for conformance tests of low level communication protocols
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Neural Processing Letters
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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