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.     «

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 develo...     »