Gesellschaft für Informatik e.V.

Lecture Notes in Informatics


German Conference on Bioinformatics 2004, GCB 2004, October 4-6, 2004, Bielefeld, Germany P-53, 95-104 (2004).

GI, Gesellschaft für Informatik, Bonn
2004


Editors

Robert Giegerich (ed.), Jens Stoye (ed.)


Copyright © GI, Gesellschaft für Informatik, Bonn

Contents

Independent components analysis of starch deficient pgm mutants

Matthias Scholz , Yves Gibon , Mark Stitt and Joachim Selbig

Abstract


Changes in enzymatic activities in response to carbon starvation were investigated in Arabidopsis thaliana in two distinct experiments. One compares the Columbia ecotype (Col-0) and its starch deficient pgm mutant (plastidial phosphoglucomutase), the other investigates the enzymatic activities of Col-0 under extended night conditions. A classical technique for detecting and visualizing relevant information from the measured data is principal component analysis (PCA). We show that independent component analysis (ICA) is more suitable for our questions and the results are more precise than those obtained with PCA. This higher informative power is only achieved when ICA is combined with suitable pre-processing and evaluation criteria. It is essential to first reduce the dimensionality of the data set, using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The measure of kurtosis is used to sort the extracted components. We found that ICA could detect on the one hand the time component of the extended night experiment, and on the other hand a discriminating component in the pgm mutant experiment. In both components the most important enzymes were the same, confirming the carbon starvation phenotype in the mutant.


Full Text: PDF

GI, Gesellschaft für Informatik, Bonn
ISBN 3-88579-382-2


Last changed 04.10.2013 18:03:55