K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

Background: 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 variation. Results: 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 crossvalidation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Max Bylesjö, Mattias Rantalainen, Jeremy K. Nicholson, Elaine Holmes, Johan Trygg
• Keywords: Biological, Biological Data, BMCBI 2008, Orthogonal Projections