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Open Access Methodology article

Simple integrative preprocessing preserves what is shared in data sources

Abhishek Tripathi12*, Arto Klami23 and Samuel Kaski23

Author Affiliations

1 Department of Computer Science, P.O. Box 68, FI-00014, University of Helsinki, Finland

2 Helsinki Institute for Information Technology, Finland

3 Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 HUT, Finland

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BMC Bioinformatics 2008, 9:111  doi:10.1186/1471-2105-9-111

Published: 21 February 2008



Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source.


It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. Source-specific variation is discarded as uninteresting. We give the details and implement them in a software tool. The method is demonstrated on gene expression measurements in three case studies: classification of cell cycle regulated genes in yeast, identification of differentially expressed genes in leukemia, and defining stress response in yeast. The software package is available at webcite.


We introduced a method for the task of data fusion for exploratory data analysis, when statistical dependencies between the sources and not within a source are interesting. The method uses canonical correlation analysis in a new way for dimensionality reduction, and inherits its good properties of being simple, fast, and easily interpretable as a linear projection.