Analysing Periodic Phenomena by Circular PCA

  • Matthias Scholz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4414)

Abstract

Experimental time courses often reveal a nonlinear behaviour. Analysing these nonlinearities is even more challenging when the observed phenomenon is cyclic or oscillatory. This means, in general, that the data describe a circular trajectory which is caused by periodic gene regulation.

Nonlinear PCA (NLPCA) is used to approximate this trajectory by a curve referred to as nonlinear component. Which, in order to analyse cyclic phenomena, must be a closed curve hence a circular component. Here, a neural network with circular units is used to generate circular components.

This circular PCA is applied to gene expression data of a time course of the intraerythrocytic developmental cycle (IDC) of the malaria parasite Plasmodium falciparum. As a result, circular PCA provides a model which describes continuously the transcriptional variation throughout the IDC. Such a computational model can then be used to comprehensively analyse the molecular behaviour over time including the identification of relevant genes at any chosen time point.

Keywords

gene expression nonlinear PCA neural networks  nonlinear dimensionality reduction  Plasmodium falciparum 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Matthias Scholz
    • 1
  1. 1.Competence Centre for Functional Genomics (CC-FG), Institute for Microbiology, Ernst-Moritz-Arndt-University GreifswaldGermany

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