Default wrapper function for the GeneNet network inference algorithm
Numeric matrix with the microarray dataset to infer the network. Columns contain variables and rows contain samples.
GeneNEt uses an heuristic for learning statistically a causal network. It relies on a conversion of a network inferred through correlation into a partial correlation graph. Then, a partial ordering of the nodes is assigned by means of a multiple testing of the log-ratio of standardized partial variances. This allows identifying a directed acyclic causal network as a sub-graph of the partial correlation network.
GeneNet.wrap The function returns a matrix which is the weighted
adjacency matrix of the network inferred by GeneNet algorithm.
The shrinkage method used to estimate the partial correlation matrix is
"static". - see
The probability threshold is set to 0.8.
Opgen-Rhein, Rainer, and Korbinian Strimmer. "Inferring gene dependency networks from genomic longitudinal data: a functional data approach." RevStat 4.1 (2006): 53-65.
Opgen-Rhein, Rainer, and Korbinian Strimmer. "Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data." Proceedings of the 4th International Workshop on Computational Systems Biology (WCSB 2006), Tampere. Vol. 4. 2006.
Sch\"afer, Juliane, and Korbinian Strimmer. "A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics." Statistical applications in genetics and molecular biology 4.1 (2005): 32.
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