Title |
Cluster and propensity based approximation of a network
|
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Published in |
BMC Systems Biology, March 2013
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DOI | 10.1186/1752-0509-7-21 |
Pubmed ID | |
Authors |
John Michael Ranola, Peter Langfelder, Kenneth Lange, Steve Horvath |
Abstract |
The models in this article generalize current models for both correlation networks and multigraph networks. Correlation networks are widely applied in genomics research. In contrast to general networks, it is straightforward to test the statistical significance of an edge in a correlation network. It is also easy to decompose the underlying correlation matrix and generate informative network statistics such as the module eigenvector. However, correlation networks only capture the connections between numeric variables. An open question is whether one can find suitable decompositions of the similarity measures employed in constructing general networks. Multigraph networks are attractive because they support likelihood based inference. Unfortunately, it is unclear how to adjust current statistical methods to detect the clusters inherent in many data sets. |
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