Open Access Methodology article

Joint mapping of genes and conditions via multidimensional unfolding analysis

Katrijn Van Deun1*, Kathleen Marchal2, Willem J Heiser3, Kristof Engelen2 and Iven Van Mechelen1

Author Affiliations

1 SymBioSys, Catholic University of Leuven, 3000 Leuven, Belgium

2 Department of Microbial and Molecular Systems, Catholic University of Leuven, 3000 Leuven, Belgium

3 Department of Psychology, Leiden University, 2300 RB Leiden, The Netherlands

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BMC Bioinformatics 2007, 8:181  doi:10.1186/1471-2105-8-181

Published: 5 June 2007



Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding.


We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types.


Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.