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SUMMARY:Mining differential correlation - Andrew Nobel (University of Nort
 h Carolina )
DTSTART:20161215T093000Z
DTEND:20161215T103000Z
UID:TALK69511@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Given data obtained under two sampling conditions\, it i
 s often of interest to identify variables that behave differently in one c
 ondition than in the other. The talk will describe a method for differenti
 al analysis of second-order behavior called Differential Correlation Minin
 g (DCM). DCM is a special case of differential analysis for weighted netwo
 rks\, and is distinct from standard analyses of first order differential b
 ehavior\, for example studies of differential expression.<br> <br> The DCM
  method identifies differentially correlated sets of variables\, with the 
 property that the average pairwise correlation between variables in a set 
 is higher under one sample condition than the other. DCM is based on an it
 erative testing procedure that adaptively updates the size and elements of
  a candidate variable set. Updates are performed via hypothesis testing of
  individual variables\, based on the asymptotic distribution of their aver
 age differential correlation. The method does not assume that the sample o
 r population correlation matrices are sparse\, or have any particular stru
 cture.<br> <br> I will present both simulation results and applications of
  DCM to genomics and brain imaging. &nbsp\;As time permits\, I will also p
 resent a brief overview of some additional network related work being done
  with collaborators at UNC.<br> <br> </span>
LOCATION:Seminar Room 1\, Newton Institute
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