TY  - JOUR
A1  - Feher, Kristen
A1  - Whelan, James
A1  - Müller, Samuel
T1  - Assessing modularity using a random matrix theory approach
T2  - Statistical applications in genetics and molecular biology
N2  - 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.
KW  - random matrix theory
KW  - clustering
KW  - modularity
Y1  - 2011
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/37258
SN  - 2194-6302
SN  - 1544-6115
VL  - 10
IS  - 1
PB  - De Gruyter
CY  - Berlin
ER  -