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Regularized estimation of large-scale gene association networks using graphical Gaussian models |
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| Authors: | Nicole Krämer Juliane Schäfer Anne-Laure Boulesteix |
| Institution: | 1. Machine Learning/Intelligent Data Analysis Group, Berlin Institute of Technology, Franklinstr 28/29, D-10587, Berlin, Germany 2. Seminar für Statistik, ETH Zurich, CH-8092, Zurich, Switzerland 3. Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, CH-4031, Basel, Switzerland 4. Department of Statistics, University of Munich, Ludwigstr 33, D-80539, Munich, Germany 5. Computational Molecular Medicine Research Group, Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, 81377, Munich, Germany |
| Abstract: | BackgroundGraphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares. |
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