4.9.2. Principal Component Analysis (PCA) — `MDAnalysis.analysis.pca`

Authors: John Detlefs
Year: 2016
Copyright: GNU Public License v3

New in version 0.16.0.

This module contains the linear dimensions reduction method Principal Component Analysis (PCA). PCA sorts a simulation into 3N directions of descending variance, with N being the number of atoms. These directions are called the principal components. The dimensions to be analyzed are reduced by only looking at a few projections of the first principal components. To learn how to run a Principal Component Analysis, please refer to the PCA Tutorial.

The PCA problem is solved by solving the eigenvalue problem of the covariance matrix, a \(3N \times 3N\) matrix where the element \((i, j)\) is the covariance between coordinates \(i\) and \(j\). The principal components are the eigenvectors of this matrix.

For each eigenvector, its eigenvalue is the variance that the eigenvector explains. Stored in PCA.results.cumulated_variance, a ratio for each number of eigenvectors up to index \(i\) is provided to quickly find out how many principal components are needed to explain the amount of variance reflected by those \(i\) eigenvectors. For most data, PCA.results.cumulated_variance will be approximately equal to one for some \(n\) that is significantly smaller than the total number of components. These are the components of interest given by Principal Component Analysis.

From here, we can project a trajectory onto these principal components and attempt to retrieve some structure from our high dimensional data.

For a basic introduction to the module, the PCA Tutorial shows how to perform Principal Component Analysis.

4.9.2.1. PCA Tutorial

The example uses files provided as part of the MDAnalysis test suite (in the variables PSF and DCD). This tutorial shows how to use the PCA class.

First load all modules and test data:

import MDAnalysis as mda
import MDAnalysis.analysis.pca as pca
from MDAnalysis.tests.datafiles import PSF, DCD

Given a universe containing trajectory data we can perform Principal Component Analyis by using the class PCA and retrieving the principal components.:

u = mda.Universe(PSF, DCD)
PSF_pca = pca.PCA(u, select='backbone')
PSF_pca.run()

Inspect the components to determine the principal components you would like to retain. The choice is arbitrary, but I will stop when 95 percent of the variance is explained by the components. This cumulated variance by the components is conveniently stored in the one-dimensional array attribute PCA.results.cumulated_variance. The value at the ith index of PCA.results.cumulated_variance is the sum of the variances from 0 to i.:

n_pcs = np.where(PSF_pca.results.cumulated_variance > 0.95)[0][0]
atomgroup = u.select_atoms('backbone')
pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)

From here, inspection of the pca_space and conclusions to be drawn from the data are left to the user.

4.9.2.2. Classes and Functions

class MDAnalysis.analysis.pca.PCA(universe, select='all', align=False, mean=None, n_components=None, **kwargs)[source]

Principal component analysis on an MD trajectory.

After initializing and calling method with a universe or an atom group, principal components ordering the atom coordinate data by decreasing variance will be available for analysis. As an example::

pca = PCA(universe, select='backbone').run()
pca_space = pca.transform(universe.select_atoms('backbone'), 3)

generates the principal components of the backbone of the atomgroup and then transforms those atomgroup coordinates by the direction of those variances. Please refer to the PCA Tutorial for more detailed instructions.

Parameters

universe (Universe) – Universe
select (string, optional) – A valid selection statement for choosing a subset of atoms from the atomgroup.
align (boolean, optional) – If True, the trajectory will be aligned to a reference structure.
mean (array_like, optional) – Optional reference positions to be be used as the mean of the covariance matrix.
n_components (int, optional) – The number of principal components to be saved, default saves all principal components
verbose (bool (optional)) – Show detailed progress of the calculation if set to True.

results.p_components

Principal components of the feature space, representing the directions of maximum variance in the data. The column vector p_components[:, i] is the eigenvector corresponding to the variance[i].

New in version 2.0.0.

Type: array, (n_atoms * 3, n_components)

p_components

Alias to the results.p_components.

Deprecated since version 2.0.0: Will be removed in MDAnalysis 3.0.0. Please use results.p_components instead.

Type: array, (n_atoms * 3, n_components)

results.variance

Raw variance explained by each eigenvector of the covariance matrix.

New in version 2.0.0.

Type: array (n_components, )

variance

Alias to the results.variance.

Deprecated since version 2.0.0: Will be removed in MDAnalysis 3.0.0. Please use results.variance instead.

Type: array (n_components, )

results.cumulated_variance

Percentage of variance explained by the selected components and the sum of the components preceding it. If a subset of components is not chosen then all components are stored and the cumulated variance will converge to 1.

New in version 2.0.0.

Type: array, (n_components, )

cumulated_variance

Alias to the results.cumulated_variance.

Deprecated since version 2.0.0: Will be removed in MDAnalysis 3.0.0. Please use results.cumulated_variance instead.

Type: array, (n_components, )

Notes

Computation can be sped up by supplying precalculated mean positions.

Changed in version 0.19.0: The start frame is used when performing selections and calculating mean positions. Previously the 0th frame was always used.

Changed in version 1.0.0: n_components now limits the correct axis of p_components. cumulated_variance now accurately represents the contribution of each principal component and does not change when n_components is given. If n_components is not None or is less than the number of p_components, cumulated_variance will not sum to 1. align=True now correctly aligns the trajectory and computes the correct means and covariance matrix.

Changed in version 2.0.0: mean_atoms removed, as this did not reliably contain the mean positions. mean input now accepts coordinate arrays instead of atomgroup. p_components, variance and cumulated_variance are now stored in a MDAnalysis.analysis.base.Results instance.

cumulative_overlap(other, i=0, n_components=None)[source]

Compute the cumulative overlap of a vector in a subspace.

This is not symmetric. The cumulative overlap measures the overlap of the chosen vector in this instance, in the other subspace.

Please cite [Yang2008] if you use this function.

Parameters

other (PCA) – Another PCA class. This must have already been run.
i (int, optional) – The index of eigenvector to be analysed.
n_components (int, optional) – number of components in other to compute for the cumulative overlap. None computes all of them.

Returns

Cumulative overlap of the chosen vector in this instance to the other subspace. 0 indicates that they are mutually orthogonal, whereas 1 indicates that they are identical.

Return type

float

4.9.2. Principal Component Analysis (PCA) — MDAnalysis.analysis.pca

4.9.2.1. PCA Tutorial

4.9.2.2. Classes and Functions

4.9.2. Principal Component Analysis (PCA) — `MDAnalysis.analysis.pca`