The eigenvectors are the principal components, and their eigenvalues are the associated variance. Optionally, you can provide a mean otherwise the covariance is to the averaged structure over the trajectory.ĭiagonalise the covariance matrix. Optionally align each frame in your trajectory to the first frame.Ĭonstruct a 3N x 3N covariance for the N atoms in your trajectory. In MDAnalysis, the method implemented in the PCA class ( API docs) is as follows: You can thereby visualise the motion described by that component. Trajectory coordinates can be transformed onto a lower-dimensional space ( essential subspace) constructed from these principal components in order to compare conformations. Please see, ,, or for a more in-depth introduction to PCA. The frame-by-frameĬonformational fluctuation can be considered a linear combination of the essential dynamics yielded by the PCA. The vectors are called principal components, and they are ordered such that the first principal component accounts for the most variance in the original data (i.e. the largest uncorrelated motion in your trajectory), and each successive component accounts for less and less variance. Principal component analysis is a common linear dimensionality reduction technique that maps the coordinates in each frame of your trajectory to a linear combination of orthogonal vectors. Universe ( PSF, DCD ) Principal component analysis ¶ Standard residues in MDAnalysis selections.Non-linear dimension reduction to diffusion maps.Measuring convergence with cosine content.Visualising projections into a reduced dimensional space.Principal component analysis of a trajectory.Constructing, modifying, and adding to a Universe.
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