Bylesjö M, Rantalainen M, Nicholson JK, Holmes E, Trygg J
K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space
BMC Bioinformatics 2008 9, 106:1-7
Kernel-based classification and regression methods have been
successfully applied to modelling a wide variety of biological data.
The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)
method offers unique properties facilitating separate modelling of
predictive variation and structured noise in the feature space. While
providing prediction results similar to other kernel-based methods,
K-OPLS features enhanced interpretational capabilities; allowing
detection of unanticipated systematic variation in the data such as
instrumental drift, batch variability or unexpected biological
We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/.
The package includes essential functionality and documentation for
model evaluation (using cross-validation), training and prediction of
future samples. Incorporated is also a set of diagnostic tools and plot
functions to simplify the visualisation of data, e.g. for detecting
trends or for identification of outlying samples. The utility of the
software package is demonstrated by means of a metabolic profiling data
set from a biological study of hybrid aspen.
The properties of the K-OPLS method are well suited for analysis of
biological data, which in conjunction with the availability of the
outlined open-source package provides a comprehensive solution for
kernel-based analysis in bioinformatics applications.
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