ccKOPLS: Confounder-correcting kernel-based orthogonal projections to latent structures

Building accurate predictive models for biological data sets from next-generation high-throughput data sources is essential to bioinformatics. However, confounding variables such as sex, age, and habitat can skew the results of such models, leading to biased and inaccurate results. While Li et. al have developed a confounder-correcting framework for Support Vector Machines (SVMs) [1], there is no such method available for machine learning algorithms suited for high-dimensional data sets with small sample sizes (d≫n). We have extended Li et. al´s confounder-correcting (cc) algorithm (ccSVM) to allow Kernel Orthogonal Projections to Latent Structures (KOPLS) to explicitly account for confounding factors. We demonstrate that our novel method complements and improves the accuracy of a non-cc KOPLS with implicit orthogonal signal correction. Finally, we show that ccKOPLS is better suited to high-dimensionality data sets than ccSVM.