Integrating gene expression and GO classification for PCA by preclustering |
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| Authors: | Jorn R De Haan Ester Piek Rene C van Schaik Jacob de Vlieg Susanne Bauerschmidt Lutgarde MC Buydens Ron Wehrens |
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| Affiliation: | (1) Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, The Netherlands;(2) Department of Applied Biology, Faculty of Science, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, The Netherlands;(3) MSD, Molenstraat 110, 5340 BH Oss, The Netherlands;(4) Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, Geert Grooteplein 28, 6525, GA, Nijmegen, The Netherlands |
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| Abstract: | Background Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering. |
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