37258
2011
2011
eng
36
1
10
article
De Gruyter
Berlin
1
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Assessing modularity using a random matrix theory approach
Random matrix theory (RMT) is well suited to describing the emergent properties of systems with complex interactions amongst their constituents through their eigenvalue spectrums. Some RMT results are applied to the problem of clustering high dimensional biological data with complex dependence structure amongst the variables. It will be shown that a gene relevance or correlation network can be constructed by choosing a correlation threshold in a principled way, such that it corresponds to a block diagonal structure in the correlation matrix, if such a structure exists. The structure is then found using community detection algorithms, but with parameter choice guided by RMT predictions. The resulting clustering is compared to a variety of hierarchical clustering outputs and is found to the most generalised result, in that it captures all the features found by the other considered methods.
Statistical applications in genetics and molecular biology
10.2202/1544-6115.1667
2194-6302 (print)
1544-6115 (online)
wos:2011-2013
44
WOS:000295515800003
Feher, K (reprint author), Univ Potsdam, Dept Biol & Biochem, Potsdam, Germany.
Marie Curie Early Stage Training Fellowship Programme
[MEST-CT-2004-513973 BIOSTAR]
Kristen Feher
James Whelan
Samuel Müller
eng
uncontrolled
random matrix theory
eng
uncontrolled
clustering
eng
uncontrolled
modularity
Institut für Biochemie und Biologie
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