A set of utilities to quantify separations between classes of observations in the scores of principal component analysis (PCA), partial least squares (PLS) and orthogonal projections to latent structures (OPLS) models. The original peer-reviewed publication introducing pca-utils in published in:
Worley, B., Powers, R., Utilities for Quantifying Separation in PCA/PLS-DA Scores, Analytical Biochemistry, 2013, 433(2): 102-104.
Modeling algorithms like PCA, PLS and OPLS project a set of K-variate observations into a low-dimensional "latent" space. In this space, the original observations are represented by points (scores), and distances between the points are related to the original distances between the high-dimensional observations.
Some obvious questions that arise when discriminating between classes are:
- How far apart are classes in scores space?
- Are the scores-space separations significant?
- Is there a higher pattern to the separations?
The pca-utils project provides a set of executables that answer these questions. The executables allow for generating dendrograms, distance matrices, class ellipses and ellipsoids based on a set of scores.
This software is highly portable. As long as you have a recent enough glibc, pca-utils should compile and install without incident. You can build and install pca-utils as follows:
git clone git://github.com/geekysuavo/pca-utils.git
sudo make install
By default, the pca-utils binaries will be installed into /usr/bin, and their manual pages will be installed into /usr/share/man/man1. If you need to install somewhere else, you'll need to modify the Makefile.
For more information on how to use the pca-utils binaries, please consult the manual pages.
The pca-utils project is released under the GNU GPL 3.0.
The idea is to advance the state of the art in the field, so as long as you adhere to the requirements of the above license, just have fun with the code!