Second-order source separation based on prior knowledge realized in a graph model

Authors:
Florian Blöchl;Andreas Kowarsch;Fabian J. Theis
Affiliations:
Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany and Equal contributors;Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany and Equal contributors;Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany and Institute for Mathematical Sciences, TU München, Garching, Germany
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

Matrix factorization techniques provide efficient tools for the detailed analysis of large-scale biological and biomedical data. While underlying algorithms usually work fully blindly, we propose to incorporate prior knowledge encoded in a graph model. This graph introduces a partial ordering in data without intrinsic (e.g. temporal or spatial) structure, which allows the definition of a graph-autocorrelation function. Using this framework as constraint to the matrix factorization task we develop a second-order source separation algorithm called graph-decorrelation algorithm (GraDe). We demonstrate its applicability and robustness by analyzing microarray data from a stem cell differentiation experiment.