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SUMMARY:Principal Nested Shape Space Analysis of Molecular Dynamics Data -
  Ian Dryden (Nottingham)
DTSTART:20161118T160000Z
DTEND:20161118T170000Z
UID:TALK67490@talks.cam.ac.uk
CONTACT:Quentin Berthet
DESCRIPTION:Molecular dynamics simulations produce huge datasets of tempor
 al sequences of molecules. It is of interest to summarize the shape evolut
 ion of the molecules in a succinct\, low-dimensional representation. Howev
 er\, Euclidean techniques such as principal components analysis (PCA) can 
 be problematic as the data may lie on a manifold which is far from being f
 lat. Principal nested spheres involves the backwards fitting of a sequence
  of nested spheres to data\, and can lead to striking insights which may b
 e missed using PCA (Jung\, Dryden and Marron\, 2012\, Biometrika). We deve
 lop principal nested shape spaces (PNSS) for three-dimensional shape data\
 , and provide some fast fitting algorithms. The methodology is applied to 
 a large set of 100 runs of 3D protein simulations\, investigating biochemi
 cal function in applications in Pharmaceutical Sciences. The data exhibit 
 distinct clusters\, representing different molecular states\, and these fe
 atures are far more apparent using PNSS compared to PCA.\n \nThis is joint
  work with Huiling Le and Kwang-Rae Kim (University of Nottingham).
LOCATION:MR12\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge.
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